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fix: redundant files are removed.

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Saeed Ranjbar 2024-10-31 09:46:33 +00:00
parent 2e7723c65f
commit 73b779747e
43 changed files with 16 additions and 5248 deletions
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45
building_modelling/ep_workflow.py
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import glob
import os
import sys
from pathlib import Path
import csv
from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
from hub.imports.results_factory import ResultFactory
sys.path.append('../energy_system_modelling_package/')
def energy_plus_workflow(city, output_path):
try:
# city = city
out_path = output_path
files = glob.glob(f'{out_path}/*')
# for file in files:
# if file != '.gitignore':
# os.remove(file)
area = 0
volume = 0
for building in city.buildings:
volume = building.volume
for ground in building.grounds:
area += ground.perimeter_polygon.area
print('exporting:')
_idf = EnergyBuildingsExportsFactory('idf', city, out_path).export()
print(' idf exported...')
_idf.run()
csv_file = str((out_path / f'{city.name}_out.csv').resolve())
eso_file = str((out_path / f'{city.name}_out.eso').resolve())
idf_file = str((out_path / f'{city.name}.idf').resolve())
obj_file = str((out_path / f'{city.name}.obj').resolve())
ResultFactory('energy_plus_multiple_buildings', city, out_path).enrich()
except Exception as ex:
print(ex)
print('error: ', ex)
print('[simulation abort]')
sys.stdout.flush()

37
building_modelling/geojson_creator.py
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import json
from shapely import Polygon
from shapely import Point
from pathlib import Path
def process_geojson(x, y, diff, expansion=False):
selection_box = Polygon([[x + diff, y - diff],
[x - diff, y - diff],
[x - diff, y + diff],
[x + diff, y + diff]])
geojson_file = Path('./data/collinear_clean 2.geojson').resolve()
if not expansion:
output_file = Path('./input_files/output_buildings.geojson').resolve()
else:
output_file = Path('./input_files/output_buildings_expanded.geojson').resolve()
buildings_in_region = []
with open(geojson_file, 'r') as file:
city = json.load(file)
buildings = city['features']
for building in buildings:
coordinates = building['geometry']['coordinates'][0]
building_polygon = Polygon(coordinates)
centroid = Point(building_polygon.centroid)
if centroid.within(selection_box):
buildings_in_region.append(building)
output_region = {"type": "FeatureCollection",
"features": buildings_in_region}
with open(output_file, 'w') as file:
file.write(json.dumps(output_region, indent=2))
return output_file

417
costing_package/capital_costs.py
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"""
Capital costs module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import math
import pandas as pd
import numpy_financial as npf
from hub.city_model_structure.building import Building
import hub.helpers.constants as cte
from costing_package.configuration import Configuration
from costing_package.constants import (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV,
SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS, PV, SYSTEM_RETROFIT)
from costing_package.cost_base import CostBase
class CapitalCosts(CostBase):
"""
Capital costs class
"""
def __init__(self, building: Building, configuration: Configuration):
super().__init__(building, configuration)
self._yearly_capital_costs = pd.DataFrame(
index=self._rng,
columns=[
'B2010_opaque_walls',
'B2020_transparent',
'B3010_opaque_roof',
'B1010_superstructure',
'D2010_photovoltaic_system',
'D3020_simultaneous_heat_and_cooling_generating_systems',
'D3030_heating_systems',
'D3040_cooling_systems',
'D3050_distribution_systems',
'D3060_other_hvac_ahu',
'D3070_storage_systems',
'D40_dhw',
],
dtype='float'
)
self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = 0
self._yearly_capital_costs.loc[0, 'B2020_transparent'] = 0
self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = 0
self._yearly_capital_costs.loc[0, 'B1010_superstructure'] = 0
self._yearly_capital_costs.loc[0, 'D2010_photovoltaic_system'] = 0
self._yearly_capital_costs.loc[0, 'D3020_simultaneous_heat_and_cooling_generating_systems'] = 0
self._yearly_capital_costs.loc[0, 'D3030_heating_systems'] = 0
self._yearly_capital_costs.loc[0, 'D3040_cooling_systems'] = 0
self._yearly_capital_costs.loc[0, 'D3050_distribution_systems'] = 0
self._yearly_capital_costs.loc[0, 'D3060_other_hvac_ahu'] = 0
self._yearly_capital_costs.loc[0, 'D3070_storage_systems'] = 0
self._yearly_capital_costs.loc[0, 'D40_dhw'] = 0
self._yearly_capital_incomes = pd.DataFrame(
index=self._rng,
columns=[
'Subsidies construction',
'Subsidies HVAC',
'Subsidies PV'
],
dtype='float'
)
self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = 0
self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = 0
self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = 0
self._yearly_capital_costs.fillna(0, inplace=True)
self._own_capital = 1 - self._configuration.percentage_credit
self._surface_pv = 0
for roof in self._building.roofs:
self._surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
for roof in self._building.roofs:
if roof.installed_solar_collector_area is not None:
self._surface_pv += roof.installed_solar_collector_area
else:
self._surface_pv += roof.solid_polygon.area * roof.solar_collectors_area_reduction_factor
def calculate(self) -> tuple[pd.DataFrame, pd.DataFrame]:
self.skin_capital_cost()
self.energy_system_capital_cost()
self.skin_yearly_capital_costs()
self.yearly_energy_system_costs()
self.yearly_incomes()
return self._yearly_capital_costs, self._yearly_capital_incomes
def skin_capital_cost(self):
"""
calculating skin costs
:return:
"""
surface_opaque = 0
surface_transparent = 0
surface_roof = 0
surface_ground = 0
for thermal_zone in self._building.thermal_zones_from_internal_zones:
for thermal_boundary in thermal_zone.thermal_boundaries:
if thermal_boundary.type == 'Ground':
surface_ground += thermal_boundary.opaque_area
elif thermal_boundary.type == 'Roof':
surface_roof += thermal_boundary.opaque_area
elif thermal_boundary.type == 'Wall':
surface_opaque += thermal_boundary.opaque_area * (1 - thermal_boundary.window_ratio)
surface_transparent += thermal_boundary.opaque_area * thermal_boundary.window_ratio
chapter = self._capital_costs_chapter.chapter('B_shell')
capital_cost_opaque = surface_opaque * chapter.item('B2010_opaque_walls').refurbishment[0]
capital_cost_transparent = surface_transparent * chapter.item('B2020_transparent').refurbishment[0]
capital_cost_roof = surface_roof * chapter.item('B3010_opaque_roof').refurbishment[0]
capital_cost_ground = surface_ground * chapter.item('B1010_superstructure').refurbishment[0]
if self._configuration.retrofit_scenario not in (SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS, PV, SYSTEM_RETROFIT):
self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = capital_cost_opaque * self._own_capital
self._yearly_capital_costs.loc[0, 'B2020_transparent'] = capital_cost_transparent * self._own_capital
self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = capital_cost_roof * self._own_capital
self._yearly_capital_costs.loc[0, 'B1010_superstructure'] = capital_cost_ground * self._own_capital
capital_cost_skin = capital_cost_opaque + capital_cost_ground + capital_cost_transparent + capital_cost_roof
return capital_cost_opaque, capital_cost_transparent, capital_cost_roof, capital_cost_ground, capital_cost_skin
def skin_yearly_capital_costs(self):
skin_capital_cost = self.skin_capital_cost()
for year in range(1, self._configuration.number_of_years):
self._yearly_capital_costs.loc[year, 'B2010_opaque_walls'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
skin_capital_cost[0] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'B2020_transparent'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
skin_capital_cost[1] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'B3010_opaque_roof'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
skin_capital_cost[2] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'B1010_superstructure'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
skin_capital_cost[3] * self._configuration.percentage_credit
)
)
def energy_system_capital_cost(self):
chapter = self._capital_costs_chapter.chapter('D_services')
system_components, component_categories, component_sizes = self.system_components()
capital_cost_heating_and_cooling_equipment = 0
capital_cost_heating_equipment = 0
capital_cost_cooling_equipment = 0
capital_cost_domestic_hot_water_equipment = 0
capital_cost_energy_storage_equipment = 0
capital_cost_distribution_equipment = 0
capital_cost_lighting = 0
capital_cost_pv = self._surface_pv * chapter.item('D2010_photovoltaic_system').initial_investment[0]
for (i, component) in enumerate(system_components):
if component_categories[i] == 'multi_generation':
capital_cost_heating_and_cooling_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
elif component_categories[i] == 'heating':
capital_cost_heating_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
elif component_categories[i] == 'cooling':
capital_cost_cooling_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
elif component_categories[i] == 'dhw':
capital_cost_domestic_hot_water_equipment += chapter.item(component).initial_investment[0] * \
component_sizes[i]
elif component_categories[i] == 'distribution':
capital_cost_distribution_equipment += chapter.item(component).initial_investment[0] * \
component_sizes[i]
else:
capital_cost_energy_storage_equipment += chapter.item(component).initial_investment[0] * component_sizes[i]
if self._configuration.retrofit_scenario in (SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, PV):
self._yearly_capital_costs.loc[0, 'D2010_photovoltaic_system'] = capital_cost_pv
if (self._configuration.retrofit_scenario in
(SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT)):
self._yearly_capital_costs.loc[0, 'D3020_simultaneous_heat_and_cooling_generating_systems'] = (
capital_cost_heating_and_cooling_equipment * self._own_capital)
self._yearly_capital_costs.loc[0, 'D3030_heating_systems'] = (
capital_cost_heating_equipment * self._own_capital)
self._yearly_capital_costs.loc[0, 'D3040_cooling_systems'] = (
capital_cost_cooling_equipment * self._own_capital)
self._yearly_capital_costs.loc[0, 'D3050_distribution_systems'] = (
capital_cost_distribution_equipment * self._own_capital)
self._yearly_capital_costs.loc[0, 'D3070_storage_systems'] = (
capital_cost_energy_storage_equipment * self._own_capital)
self._yearly_capital_costs.loc[0, 'D40_dhw'] = (
capital_cost_domestic_hot_water_equipment * self._own_capital)
capital_cost_hvac = (capital_cost_heating_and_cooling_equipment + capital_cost_distribution_equipment +
capital_cost_energy_storage_equipment + capital_cost_domestic_hot_water_equipment)
return (capital_cost_pv, capital_cost_heating_and_cooling_equipment, capital_cost_heating_equipment,
capital_cost_distribution_equipment, capital_cost_cooling_equipment, capital_cost_energy_storage_equipment,
capital_cost_domestic_hot_water_equipment, capital_cost_lighting, capital_cost_hvac)
def yearly_energy_system_costs(self):
chapter = self._capital_costs_chapter.chapter('D_services')
system_investment_costs = self.energy_system_capital_cost()
system_components, component_categories, component_sizes = self.system_components()
pv = False
for energy_system in self._building.energy_systems:
for generation_system in energy_system.generation_systems:
if generation_system.system_type == cte.PHOTOVOLTAIC:
pv = True
for year in range(1, self._configuration.number_of_years):
costs_increase = math.pow(1 + self._configuration.consumer_price_index, year)
self._yearly_capital_costs.loc[year, 'D2010_photovoltaic_system'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[0] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'D3020_simultaneous_heat_and_cooling_generating_systems'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[1] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'D3030_heating_systems'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[2] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'D3040_cooling_systems'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[3] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'D3050_distribution_systems'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[4] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'D3070_storage_systems'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[5] * self._configuration.percentage_credit
)
)
self._yearly_capital_costs.loc[year, 'D40_dhw'] = (
-npf.pmt(
self._configuration.interest_rate,
self._configuration.credit_years,
system_investment_costs[6] * self._configuration.percentage_credit
)
)
if self._configuration.retrofit_scenario not in (SKIN_RETROFIT, PV):
for (i, component) in enumerate(system_components):
if (year % chapter.item(component).lifetime) == 0 and year != (self._configuration.number_of_years - 1):
if component_categories[i] == 'multi_generation':
reposition_cost_heating_and_cooling_equipment = (chapter.item(component).reposition[0] *
component_sizes[i] * costs_increase)
self._yearly_capital_costs.loc[year, 'D3020_simultaneous_heat_and_cooling_generating_systems'] += (
reposition_cost_heating_and_cooling_equipment)
elif component_categories[i] == 'heating':
reposition_cost_heating_equipment = (chapter.item(component).reposition[0] *
component_sizes[i] * costs_increase)
self._yearly_capital_costs.loc[year, 'D3030_heating_systems'] += (
reposition_cost_heating_equipment)
elif component_categories[i] == 'cooling':
reposition_cost_cooling_equipment = (chapter.item(component).reposition[0] *
component_sizes[i] * costs_increase)
self._yearly_capital_costs.loc[year, 'D3040_cooling_systems'] += (
reposition_cost_cooling_equipment)
elif component_categories[i] == 'dhw':
reposition_cost_domestic_hot_water_equipment = (
chapter.item(component).reposition[0] * component_sizes[i] * costs_increase)
self._yearly_capital_costs.loc[year, 'D40_dhw'] += reposition_cost_domestic_hot_water_equipment
elif component_categories[i] == 'distribution':
reposition_cost_distribution_equipment = (
chapter.item(component).reposition[0] * component_sizes[i] * costs_increase)
self._yearly_capital_costs.loc[year, 'D3050_distribution_systems'] += (
reposition_cost_distribution_equipment)
else:
reposition_cost_energy_storage_equipment = (
chapter.item(component).initial_investment[0] * component_sizes[i] * costs_increase)
self._yearly_capital_costs.loc[year, 'D3070_storage_systems'] += reposition_cost_energy_storage_equipment
if self._configuration.retrofit_scenario == CURRENT_STATUS and pv:
if (year % chapter.item('D2010_photovoltaic_system').lifetime) == 0:
self._yearly_capital_costs.loc[year, 'D2010_photovoltaic_system'] += (
self._surface_pv * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
)
elif self._configuration.retrofit_scenario in (PV, SYSTEM_RETROFIT_AND_PV,
SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
if (year % chapter.item('D2010_photovoltaic_system').lifetime) == 0:
self._yearly_capital_costs.loc[year, 'D2010_photovoltaic_system'] += (
self._surface_pv * chapter.item('D2010_photovoltaic_system').reposition[0] * costs_increase
)
def system_components(self):
system_components = []
component_categories = []
sizes = []
energy_systems = self._building.energy_systems
for energy_system in energy_systems:
demand_types = energy_system.demand_types
generation_systems = energy_system.generation_systems
distribution_systems = energy_system.distribution_systems
for generation_system in generation_systems:
if generation_system.system_type != cte.PHOTOVOLTAIC:
heating_capacity = generation_system.nominal_heat_output or 0
cooling_capacity = generation_system.nominal_cooling_output or 0
installed_capacity = max(heating_capacity, cooling_capacity) / 1000
if cte.DOMESTIC_HOT_WATER in demand_types and cte.HEATING not in demand_types:
component_categories.append('dhw')
sizes.append(installed_capacity)
if generation_system.system_type == cte.HEAT_PUMP:
system_components.append(self.heat_pump_type(generation_system, domestic_how_water=True))
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
system_components.append(self.boiler_type(generation_system))
else:
system_components.append('D302010_template_heat')
elif cte.HEATING in demand_types:
if cte.COOLING in demand_types and generation_system.fuel_type == cte.ELECTRICITY:
component_categories.append('multi_generation')
else:
component_categories.append('heating')
sizes.append(installed_capacity)
if generation_system.system_type == cte.HEAT_PUMP:
item_type = self.heat_pump_type(generation_system)
system_components.append(item_type)
elif generation_system.system_type == cte.BOILER:
item_type = self.boiler_type(generation_system)
system_components.append(item_type)
else:
if cooling_capacity > heating_capacity:
system_components.append('D302090_template_cooling')
else:
system_components.append('D302010_template_heat')
elif cte.COOLING in demand_types:
component_categories.append('cooling')
sizes.append(installed_capacity)
if generation_system.system_type == cte.HEAT_PUMP:
item_type = self.heat_pump_type(generation_system)
system_components.append(item_type)
else:
system_components.append('D302090_template_cooling')
if generation_system.energy_storage_systems is not None:
energy_storage_systems = generation_system.energy_storage_systems
for storage_system in energy_storage_systems:
if storage_system.type_energy_stored == 'thermal':
component_categories.append('thermal storage')
sizes.append(storage_system.volume or 0)
system_components.append('D306010_storage_tank')
if distribution_systems is not None:
for distribution_system in distribution_systems:
component_categories.append('distribution')
sizes.append(self._building.cooling_peak_load[cte.YEAR][0] / 1000)
system_components.append('D3040_distribution_systems')
return system_components, component_categories, sizes
def yearly_incomes(self):
capital_cost_skin = self.skin_capital_cost()[-1]
system_investment_cost = self.energy_system_capital_cost()
capital_cost_hvac = system_investment_cost[-1]
capital_cost_pv = system_investment_cost[0]
self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = (
capital_cost_skin * self._archetype.income.construction_subsidy/100
)
self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = capital_cost_hvac * self._archetype.income.hvac_subsidy/100
self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = capital_cost_pv * self._archetype.income.photovoltaic_subsidy/100
self._yearly_capital_incomes.fillna(0, inplace=True)
@staticmethod
def heat_pump_type(generation_system, domestic_how_water=False):
source_medium = generation_system.source_medium
supply_medium = generation_system.supply_medium
if domestic_how_water:
heat_pump_item = 'D4010_hot_water_heat_pump'
else:
if source_medium == cte.AIR and supply_medium == cte.WATER:
heat_pump_item = 'D302020_air_to_water_heat_pump'
elif source_medium == cte.AIR and supply_medium == cte.AIR:
heat_pump_item = 'D302050_air_to_air_heat_pump'
elif source_medium == cte.GROUND and supply_medium == cte.WATER:
heat_pump_item = 'D302030_ground_to_water_heat_pump'
elif source_medium == cte.GROUND and supply_medium == cte.AIR:
heat_pump_item = 'D302100_ground_to_air_heat_pump'
elif source_medium == cte.WATER and supply_medium == cte.WATER:
heat_pump_item = 'D302040_water_to_water_heat_pump'
elif source_medium == cte.WATER and supply_medium == cte.AIR:
heat_pump_item = 'D302110_water_to_air_heat_pump'
else:
heat_pump_item = 'D302010_template_heat'
return heat_pump_item
@staticmethod
def boiler_type(generation_system):
fuel = generation_system.fuel_type
if fuel == cte.ELECTRICITY:
boiler_item = 'D302080_electrical_boiler'
elif fuel == cte.GAS:
boiler_item = 'D302070_natural_gas_boiler'
else:
boiler_item = 'D302010_template_heat'
return boiler_item

238
costing_package/configuration.py
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@ -1,238 +0,0 @@
"""
Configuration module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
from hub.catalog_factories.costs_catalog_factory import CostsCatalogFactory
from hub.catalog_factories.catalog import Catalog
class Configuration:
"""
Configuration class
"""
def __init__(self,
number_of_years,
percentage_credit,
interest_rate,
credit_years,
consumer_price_index,
electricity_peak_index,
electricity_price_index,
gas_price_index,
discount_rate,
retrofitting_year_construction,
factories_handler,
retrofit_scenario,
fuel_type,
dictionary,
fuel_tariffs
):
self._number_of_years = number_of_years
self._percentage_credit = percentage_credit
self._interest_rate = interest_rate
self._credit_years = credit_years
self._consumer_price_index = consumer_price_index
self._electricity_peak_index = electricity_peak_index
self._electricity_price_index = electricity_price_index
self._gas_price_index = gas_price_index
self._discount_rate = discount_rate
self._retrofitting_year_construction = retrofitting_year_construction
self._factories_handler = factories_handler
self._costs_catalog = CostsCatalogFactory(factories_handler).catalog
self._retrofit_scenario = retrofit_scenario
self._fuel_type = fuel_type
self._dictionary = dictionary
self._fuel_tariffs = fuel_tariffs
@property
def number_of_years(self):
"""
Get number of years
"""
return self._number_of_years
@number_of_years.setter
def number_of_years(self, value):
"""
Set number of years
"""
self._number_of_years = value
@property
def percentage_credit(self):
"""
Get percentage credit
"""
return self._percentage_credit
@percentage_credit.setter
def percentage_credit(self, value):
"""
Set percentage credit
"""
self._percentage_credit = value
@property
def interest_rate(self):
"""
Get interest rate
"""
return self._interest_rate
@interest_rate.setter
def interest_rate(self, value):
"""
Set interest rate
"""
self._interest_rate = value
@property
def credit_years(self):
"""
Get credit years
"""
return self._credit_years
@credit_years.setter
def credit_years(self, value):
"""
Set credit years
"""
self._credit_years = value
@property
def consumer_price_index(self):
"""
Get consumer price index
"""
return self._consumer_price_index
@consumer_price_index.setter
def consumer_price_index(self, value):
"""
Set consumer price index
"""
self._consumer_price_index = value
@property
def electricity_peak_index(self):
"""
Get electricity peak index
"""
return self._electricity_peak_index
@electricity_peak_index.setter
def electricity_peak_index(self, value):
"""
Set electricity peak index
"""
self._electricity_peak_index = value
@property
def electricity_price_index(self):
"""
Get electricity price index
"""
return self._electricity_price_index
@electricity_price_index.setter
def electricity_price_index(self, value):
"""
Set electricity price index
"""
self._electricity_price_index = value
@property
def gas_price_index(self):
"""
Get gas price index
"""
return self._gas_price_index
@gas_price_index.setter
def gas_price_index(self, value):
"""
Set gas price index
"""
self._gas_price_index = value
@property
def discount_rate(self):
"""
Get discount rate
"""
return self._discount_rate
@discount_rate.setter
def discount_rate(self, value):
"""
Set discount rate
"""
self._discount_rate = value
@property
def retrofitting_year_construction(self):
"""
Get retrofitting year construction
"""
return self._retrofitting_year_construction
@retrofitting_year_construction.setter
def retrofitting_year_construction(self, value):
"""
Set retrofitting year construction
"""
self._retrofitting_year_construction = value
@property
def factories_handler(self):
"""
Get factories handler
"""
return self._factories_handler
@factories_handler.setter
def factories_handler(self, value):
"""
Set factories handler
"""
self._factories_handler = value
@property
def costs_catalog(self) -> Catalog:
"""
Get costs catalog
"""
return self._costs_catalog
@property
def retrofit_scenario(self):
"""
Get retrofit scenario
"""
return self._retrofit_scenario
@property
def fuel_type(self):
"""
Get fuel type (0: Electricity, 1: Gas)
"""
return self._fuel_type
@property
def dictionary(self):
"""
Get hub function to cost function dictionary
"""
return self._dictionary
@property
def fuel_tariffs(self):
"""
Get fuel tariffs
"""
return self._fuel_tariffs

23
costing_package/constants.py
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"""
Constants module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
# constants
CURRENT_STATUS = 0
SKIN_RETROFIT = 1
SYSTEM_RETROFIT_AND_PV = 2
SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV = 3
PV = 4
SYSTEM_RETROFIT = 5
RETROFITTING_SCENARIOS = [
CURRENT_STATUS,
SKIN_RETROFIT,
SYSTEM_RETROFIT_AND_PV,
SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV,
PV,
SYSTEM_RETROFIT
]

170
costing_package/cost.py
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"""
Cost module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import pandas as pd
import numpy_financial as npf
from hub.city_model_structure.building import Building
from hub.helpers.dictionaries import Dictionaries
from costing_package.configuration import Configuration
from costing_package.capital_costs import CapitalCosts
from costing_package.end_of_life_costs import EndOfLifeCosts
from costing_package.total_maintenance_costs import TotalMaintenanceCosts
from costing_package.total_operational_costs import TotalOperationalCosts
from costing_package.total_operational_incomes import TotalOperationalIncomes
from costing_package.constants import CURRENT_STATUS
import hub.helpers.constants as cte
class Cost:
"""
Cost class
"""
def __init__(self,
building: Building,
number_of_years=31,
percentage_credit=0,
interest_rate=0.04,
credit_years=15,
consumer_price_index=0.04,
electricity_peak_index=0.05,
electricity_price_index=0.05,
fuel_price_index=0.05,
discount_rate=0.03,
retrofitting_year_construction=2020,
factories_handler='montreal_new',
retrofit_scenario=CURRENT_STATUS,
dictionary=None,
fuel_tariffs=None):
if fuel_tariffs is None:
fuel_tariffs = ['Electricity-D', 'Gas-Energir']
if dictionary is None:
dictionary = Dictionaries().hub_function_to_montreal_custom_costs_function
self._building = building
fuel_type = self._building.energy_consumption_breakdown.keys()
self._configuration = Configuration(number_of_years,
percentage_credit,
interest_rate, credit_years,
consumer_price_index,
electricity_peak_index,
electricity_price_index,
fuel_price_index,
discount_rate,
retrofitting_year_construction,
factories_handler,
retrofit_scenario,
fuel_type,
dictionary,
fuel_tariffs)
@property
def building(self) -> Building:
"""
Get current building.
"""
return self._building
def _npv_from_list(self, list_cashflow):
return npf.npv(self._configuration.discount_rate, list_cashflow)
@property
def life_cycle(self) -> pd.DataFrame:
"""
Get complete life cycle costs
:return: DataFrame
"""
results = pd.DataFrame()
global_capital_costs, global_capital_incomes = CapitalCosts(self._building, self._configuration).calculate()
global_end_of_life_costs = EndOfLifeCosts(self._building, self._configuration).calculate()
global_operational_costs = TotalOperationalCosts(self._building, self._configuration).calculate()
global_maintenance_costs = TotalMaintenanceCosts(self._building, self._configuration).calculate()
global_operational_incomes = TotalOperationalIncomes(self._building, self._configuration).calculate()
df_capital_costs_skin = (
global_capital_costs['B2010_opaque_walls'] +
global_capital_costs['B2020_transparent'] +
global_capital_costs['B3010_opaque_roof'] +
global_capital_costs['B1010_superstructure']
)
df_capital_costs_systems = (
global_capital_costs['D3020_simultaneous_heat_and_cooling_generating_systems'] +
global_capital_costs['D3030_heating_systems'] +
global_capital_costs['D3040_cooling_systems'] +
global_capital_costs['D3050_distribution_systems'] +
global_capital_costs['D3060_other_hvac_ahu'] +
global_capital_costs['D3070_storage_systems'] +
global_capital_costs['D40_dhw'] +
global_capital_costs['D2010_photovoltaic_system']
)
df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
operational_costs_list = [
global_operational_costs['Fixed Costs Electricity Peak'],
global_operational_costs['Fixed Costs Electricity Monthly'],
global_operational_costs['Variable Costs Electricity']
]
additional_costs = [
global_operational_costs[f'Fixed Costs {fuel}'] for fuel in
self._building.energy_consumption_breakdown.keys() if fuel != cte.ELECTRICITY
] + [
global_operational_costs[f'Variable Costs {fuel}'] for fuel in
self._building.energy_consumption_breakdown.keys() if fuel != cte.ELECTRICITY
]
df_operational_costs = sum(operational_costs_list + additional_costs)
df_maintenance_costs = (
global_maintenance_costs['Heating_maintenance'] +
global_maintenance_costs['Cooling_maintenance'] +
global_maintenance_costs['PV_maintenance']
)
df_operational_incomes = global_operational_incomes['Incomes electricity']
df_capital_incomes = (
global_capital_incomes['Subsidies construction'] +
global_capital_incomes['Subsidies HVAC'] +
global_capital_incomes['Subsidies PV']
)
life_cycle_costs_capital_skin = self._npv_from_list(df_capital_costs_skin.values.tolist())
life_cycle_costs_capital_systems = self._npv_from_list(df_capital_costs_systems.values.tolist())
life_cycle_costs_end_of_life_costs = self._npv_from_list(df_end_of_life_costs.values.tolist())
life_cycle_operational_costs = self._npv_from_list(df_operational_costs)
life_cycle_maintenance_costs = self._npv_from_list(df_maintenance_costs.values.tolist())
life_cycle_operational_incomes = self._npv_from_list(df_operational_incomes.values.tolist())
life_cycle_capital_incomes = self._npv_from_list(df_capital_incomes.values.tolist())
results[f'Scenario {self._configuration.retrofit_scenario}'] = [
life_cycle_costs_capital_skin,
life_cycle_costs_capital_systems,
life_cycle_costs_end_of_life_costs,
life_cycle_operational_costs,
life_cycle_maintenance_costs,
life_cycle_operational_incomes,
life_cycle_capital_incomes,
global_capital_costs,
global_capital_incomes,
global_end_of_life_costs,
global_operational_costs,
global_maintenance_costs,
global_operational_incomes
]
results.index = [
'total_capital_costs_skin',
'total_capital_costs_systems',
'end_of_life_costs',
'total_operational_costs',
'total_maintenance_costs',
'operational_incomes',
'capital_incomes',
'global_capital_costs',
'global_capital_incomes',
'global_end_of_life_costs',
'global_operational_costs',
'global_maintenance_costs',
'global_operational_incomes'
]
return results

40
costing_package/cost_base.py
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"""
Cost base module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
from hub.city_model_structure.building import Building
from costing_package.configuration import Configuration
class CostBase:
"""
Abstract base class for the costs
"""
def __init__(self, building: Building, configuration: Configuration):
self._building = building
self._configuration = configuration
self._total_floor_area = 0
for thermal_zone in building.thermal_zones_from_internal_zones:
self._total_floor_area += thermal_zone.total_floor_area
self._archetype = None
self._capital_costs_chapter = None
for archetype in self._configuration.costs_catalog.entries().archetypes:
if configuration.dictionary[str(building.function)] == str(archetype.function):
self._archetype = archetype
self._capital_costs_chapter = self._archetype.capital_cost
break
if not self._archetype:
raise KeyError(f'archetype not found for function {building.function}')
self._rng = range(configuration.number_of_years)
def calculate(self):
"""
Raises not implemented exception
"""
raise NotImplementedError()

38
costing_package/end_of_life_costs.py
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"""
End of life costs module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import math
import pandas as pd
from hub.city_model_structure.building import Building
from costing_package.configuration import Configuration
from costing_package.cost_base import CostBase
class EndOfLifeCosts(CostBase):
"""
End of life costs class
"""
def __init__(self, building: Building, configuration: Configuration):
super().__init__(building, configuration)
self._yearly_end_of_life_costs = pd.DataFrame(index=self._rng, columns=['End_of_life_costs'], dtype='float')
def calculate(self):
"""
Calculate end of life costs
:return: pd.DataFrame
"""
archetype = self._archetype
total_floor_area = self._total_floor_area
for year in range(1, self._configuration.number_of_years + 1):
price_increase = math.pow(1 + self._configuration.consumer_price_index, year)
if year == self._configuration.number_of_years:
self._yearly_end_of_life_costs.at[year, 'End_of_life_costs'] = (
total_floor_area * archetype.end_of_life_cost * price_increase
)
self._yearly_end_of_life_costs.fillna(0, inplace=True)
return self._yearly_end_of_life_costs

56
costing_package/peak_load.py
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"""
Peak load module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import pandas as pd
import hub.helpers.constants as cte
class PeakLoad:
"""
Peak load class
"""
def __init__(self, building):
self._building = building
@property
def electricity_peak_load(self):
"""
Get the electricity peak load in W
"""
array = [None] * 12
heating = 0
cooling = 0
for system in self._building.energy_systems:
if cte.HEATING in system.demand_types:
heating = 1
if cte.COOLING in system.demand_types:
cooling = 1
if cte.MONTH in self._building.heating_peak_load.keys() and cte.MONTH in self._building.cooling_peak_load.keys():
peak_lighting = self._building.lighting_peak_load[cte.YEAR][0]
peak_appliances = self._building.appliances_peak_load[cte.YEAR][0]
monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
conditioning_peak = max(self._building.heating_peak_load[cte.MONTH], self._building.cooling_peak_load[cte.MONTH])
for i in range(len(conditioning_peak)):
if cooling == 1 and heating == 1:
conditioning_peak[i] = conditioning_peak[i]
continue
elif cooling == 0:
conditioning_peak[i] = self._building.heating_peak_load[cte.MONTH][i] * heating
else:
conditioning_peak[i] = self._building.cooling_peak_load[cte.MONTH][i] * cooling
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
electricity_peak_load_results = pd.DataFrame(
monthly_electricity_peak,
columns=[f'electricity peak load W']
)
else:
electricity_peak_load_results = pd.DataFrame(array, columns=[f'electricity peak load W'])
return electricity_peak_load_results

138
costing_package/total_maintenance_costs.py
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"""
Total maintenance costs module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import math
import pandas as pd
from hub.city_model_structure.building import Building
import hub.helpers.constants as cte
from costing_package.configuration import Configuration
from costing_package.cost_base import CostBase
class TotalMaintenanceCosts(CostBase):
"""
Total maintenance costs class
"""
def __init__(self, building: Building, configuration: Configuration):
super().__init__(building, configuration)
self._yearly_maintenance_costs = pd.DataFrame(
index=self._rng,
columns=[
'Heating_maintenance',
'Cooling_maintenance',
'DHW_maintenance',
'PV_maintenance'
],
dtype='float'
)
def calculate(self) -> pd.DataFrame:
"""
Calculate total maintenance costs
:return: pd.DataFrame
"""
building = self._building
archetype = self._archetype
# todo: change area pv when the variable exists
roof_area = 0
surface_pv = 0
for roof in self._building.roofs:
if roof.installed_solar_collector_area is not None:
surface_pv += roof.installed_solar_collector_area
else:
surface_pv = roof_area * 0.5
energy_systems = building.energy_systems
maintenance_heating_0 = 0
maintenance_cooling_0 = 0
maintenance_dhw_0 = 0
heating_equipments = {}
cooling_equipments = {}
dhw_equipments = {}
for energy_system in energy_systems:
if cte.COOLING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
cooling_equipments['air_source_heat_pump'] = generation_system.nominal_cooling_output / 1000
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
cooling_equipments['ground_source_heat_pump'] = generation_system.nominal_cooling_output / 1000
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
cooling_equipments['water_source_heat_pump'] = generation_system.nominal_cooling_output / 1000
else:
cooling_equipments['general_cooling_equipment'] = generation_system.nominal_cooling_output / 1000
if cte.HEATING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
heating_equipments['air_source_heat_pump'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
heating_equipments['ground_source_heat_pump'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
heating_equipments['water_source_heat_pump'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
heating_equipments['gas_boiler'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
heating_equipments['electric_boiler'] = generation_system.nominal_heat_output / 1000
else:
heating_equipments['general_heating_equipment'] = generation_system.nominal_heat_output / 1000
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types and cte.HEATING not in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
dhw_equipments['air_source_heat_pump'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
dhw_equipments['ground_source_heat_pump'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
dhw_equipments['water_source_heat_pump'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
dhw_equipments['gas_boiler'] = generation_system.nominal_heat_output / 1000
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
dhw_equipments['electric_boiler'] = generation_system.nominal_heat_output / 1000
else:
dhw_equipments['general_heating_equipment'] = generation_system.nominal_heat_output / 1000
for heating_equipment in heating_equipments:
component = self.search_hvac_equipment(heating_equipment)
maintenance_cost = component.maintenance[0]
maintenance_heating_0 += (heating_equipments[heating_equipment] * maintenance_cost)
for cooling_equipment in cooling_equipments:
component = self.search_hvac_equipment(cooling_equipment)
maintenance_cost = component.maintenance[0]
maintenance_cooling_0 += (cooling_equipments[cooling_equipment] * maintenance_cost)
for dhw_equipment in dhw_equipments:
component = self.search_hvac_equipment(dhw_equipment)
maintenance_cost = component.maintenance[0]
maintenance_dhw_0 += (dhw_equipments[dhw_equipment] * maintenance_cost)
maintenance_pv_0 = surface_pv * archetype.operational_cost.maintenance_pv
for year in range(1, self._configuration.number_of_years + 1):
costs_increase = math.pow(1 + self._configuration.consumer_price_index, year)
self._yearly_maintenance_costs.loc[year, 'Heating_maintenance'] = (
maintenance_heating_0 * costs_increase
)
self._yearly_maintenance_costs.loc[year, 'Cooling_maintenance'] = (
maintenance_cooling_0 * costs_increase
)
self._yearly_maintenance_costs.loc[year, 'DHW_maintenance'] = (
maintenance_dhw_0 * costs_increase
)
self._yearly_maintenance_costs.loc[year, 'PV_maintenance'] = (
maintenance_pv_0 * costs_increase
)
self._yearly_maintenance_costs.fillna(0, inplace=True)
return self._yearly_maintenance_costs
def search_hvac_equipment(self, equipment_type):
for component in self._archetype.operational_cost.maintenance_hvac:
if component.type == equipment_type:
return component

237
costing_package/total_operational_costs.py
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"""
Total operational costs module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2024 Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import math
import pandas as pd
from hub.city_model_structure.building import Building
import hub.helpers.constants as cte
from costing_package.configuration import Configuration
from costing_package.cost_base import CostBase
from costing_package.peak_load import PeakLoad
class TotalOperationalCosts(CostBase):
"""
Total Operational costs class
"""
def __init__(self, building: Building, configuration: Configuration):
super().__init__(building, configuration)
columns_list = self.columns()
self._yearly_operational_costs = pd.DataFrame(
index=self._rng,
columns=columns_list,
dtype='float'
)
def calculate(self) -> pd.DataFrame:
"""
Calculate total operational costs
:return: pd.DataFrame
"""
building = self._building
fuel_consumption_breakdown = building.energy_consumption_breakdown
archetype = self._archetype
total_floor_area = self._total_floor_area
if archetype.function == 'residential':
factor = total_floor_area / 80
else:
factor = 1
total_electricity_consumption = sum(self._building.energy_consumption_breakdown[cte.ELECTRICITY].values()) / 3600
peak_electricity_load = PeakLoad(self._building).electricity_peak_load
peak_load_value = peak_electricity_load.max(axis=1)
peak_electricity_demand = peak_load_value[1] / 1000 # self._peak_electricity_demand adapted to kW
for system_fuel in self._configuration.fuel_type:
fuel = None
for fuel_tariff in self._configuration.fuel_tariffs:
if system_fuel in fuel_tariff:
fuel = self.search_fuel(system_fuel, fuel_tariff)
if fuel.type == cte.ELECTRICITY:
if fuel.variable.rate_type == 'fixed':
variable_electricity_cost_year_0 = (
total_electricity_consumption * float(fuel.variable.values[0]) / 1000
)
else:
hourly_electricity_consumption = self.hourly_fuel_consumption_profile(fuel.type)
hourly_electricity_price_profile = fuel.variable.values * len(hourly_electricity_consumption)
hourly_electricity_price = [hourly_electricity_consumption[i] / 1000 * hourly_electricity_price_profile[i]
for i in range(len(hourly_electricity_consumption))]
variable_electricity_cost_year_0 = sum(hourly_electricity_price)
peak_electricity_cost_year_0 = peak_electricity_demand * fuel.fixed_power * 12
monthly_electricity_cost_year_0 = fuel.fixed_monthly * 12 * factor
for year in range(1, self._configuration.number_of_years + 1):
price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
price_increase_peak_electricity = math.pow(1 + self._configuration.electricity_peak_index, year)
self._yearly_operational_costs.at[year, 'Fixed Costs Electricity Peak'] = (
peak_electricity_cost_year_0 * price_increase_peak_electricity
)
self._yearly_operational_costs.at[year, 'Fixed Costs Electricity Monthly'] = (
monthly_electricity_cost_year_0 * price_increase_peak_electricity
)
if not isinstance(variable_electricity_cost_year_0, pd.DataFrame):
variable_costs_electricity = variable_electricity_cost_year_0 * price_increase_electricity
else:
variable_costs_electricity = float(variable_electricity_cost_year_0.iloc[0] * price_increase_electricity)
self._yearly_operational_costs.at[year, 'Variable Costs Electricity'] = (
variable_costs_electricity
)
else:
fuel_fixed_cost = fuel.fixed_monthly * 12 * factor
if fuel.type == cte.BIOMASS:
conversion_factor = 1
else:
conversion_factor = fuel.density[0]
if fuel.variable.rate_type == 'fixed':
variable_cost_fuel = (
(sum(fuel_consumption_breakdown[fuel.type].values()) / (
1e6 * fuel.lower_heating_value[0] * conversion_factor)) * fuel.variable.values[0])
else:
hourly_fuel_consumption = self.hourly_fuel_consumption_profile(fuel.type)
hourly_fuel_price_profile = fuel.variable.values * len(hourly_fuel_consumption)
hourly_fuel_price = [hourly_fuel_consumption[i] / (
1e6 * fuel.lower_heating_value[0] * conversion_factor) * hourly_fuel_price_profile[i]
for i in range(len(hourly_fuel_consumption))]
variable_cost_fuel = sum(hourly_fuel_price)
for year in range(1, self._configuration.number_of_years + 1):
price_increase_gas = math.pow(1 + self._configuration.gas_price_index, year)
self._yearly_operational_costs.at[year, f'Fixed Costs {fuel.type}'] = fuel_fixed_cost * price_increase_gas
self._yearly_operational_costs.at[year, f'Variable Costs {fuel.type}'] = (
variable_cost_fuel * price_increase_gas)
self._yearly_operational_costs.fillna(0, inplace=True)
return self._yearly_operational_costs
def columns(self):
columns_list = []
fuels = [key for key in self._building.energy_consumption_breakdown.keys()]
for fuel in fuels:
if fuel == cte.ELECTRICITY:
columns_list.append('Fixed Costs Electricity Peak')
columns_list.append('Fixed Costs Electricity Monthly')
columns_list.append('Variable Costs Electricity')
else:
columns_list.append(f'Fixed Costs {fuel}')
columns_list.append(f'Variable Costs {fuel}')
return columns_list
def search_fuel(self, system_fuel, tariff):
fuels = self._archetype.operational_cost.fuels
for fuel in fuels:
if system_fuel == fuel.type and tariff == fuel.variable.name:
return fuel
raise KeyError(f'fuel {system_fuel} with {tariff} tariff not found')
def hourly_fuel_consumption_profile(self, fuel_type):
hourly_fuel_consumption = []
energy_systems = self._building.energy_systems
if fuel_type == cte.ELECTRICITY:
appliance = self._building.appliances_electrical_demand[cte.HOUR]
lighting = self._building.lighting_electrical_demand[cte.HOUR]
elec_heating = 0
elec_cooling = 0
elec_dhw = 0
if cte.HEATING in self._building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_heating = 1
if cte.COOLING in self._building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_cooling = 1
if cte.DOMESTIC_HOT_WATER in self._building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_dhw = 1
heating = None
cooling = None
dhw = None
if elec_heating == 1:
for energy_system in energy_systems:
if cte.HEATING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if cte.HEATING in generation_system.energy_consumption:
heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
heating = [x / 2 for x in self._building.heating_consumption[cte.HOUR]]
else:
heating = self._building.heating_consumption[cte.HOUR]
if elec_dhw == 1:
for energy_system in energy_systems:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
dhw = [x / 2 for x in self._building.domestic_hot_water_consumption[cte.HOUR]]
else:
dhw = self._building.domestic_hot_water_consumption[cte.HOUR]
if elec_cooling == 1:
for energy_system in energy_systems:
if cte.COOLING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if cte.COOLING in generation_system.energy_consumption:
cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
cooling = [x / 2 for x in self._building.cooling_consumption[cte.HOUR]]
else:
cooling = self._building.cooling_consumption[cte.HOUR]
for i in range(len(self._building.heating_demand[cte.HOUR])):
hourly = 0
hourly += appliance[i] / 3600
hourly += lighting[i] / 3600
if heating is not None:
hourly += heating[i] / 3600
if cooling is not None:
hourly += cooling[i] / 3600
if dhw is not None:
hourly += dhw[i] / 3600
hourly_fuel_consumption.append(hourly)
else:
heating = None
dhw = None
if cte.HEATING in self._building.energy_consumption_breakdown[fuel_type]:
for energy_system in energy_systems:
if cte.HEATING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if cte.HEATING in generation_system.energy_consumption:
heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
heating = [x / 2 for x in self._building.heating_consumption[cte.HOUR]]
else:
heating = self._building.heating_consumption[cte.HOUR]
if cte.DOMESTIC_HOT_WATER in self._building.energy_consumption_breakdown[fuel_type]:
for energy_system in energy_systems:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
dhw = [x / 2 for x in self._building.domestic_hot_water_consumption[cte.HOUR]]
else:
dhw = self._building.domestic_hot_water_consumption[cte.HOUR]
for i in range(len(self._building.heating_demand[cte.HOUR])):
hourly = 0
if heating is not None:
hourly += heating[i] / 3600
if dhw is not None:
hourly += dhw[i] / 3600
hourly_fuel_consumption.append(hourly)
return hourly_fuel_consumption

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costing_package/total_operational_incomes.py
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"""
Total operational incomes module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
import math
import pandas as pd
from hub.city_model_structure.building import Building
import hub.helpers.constants as cte
from costing_package.configuration import Configuration
from costing_package.cost_base import CostBase
class TotalOperationalIncomes(CostBase):
"""
Total operational incomes class
"""
def __init__(self, building: Building, configuration: Configuration):
super().__init__(building, configuration)
self._yearly_operational_incomes = pd.DataFrame(index=self._rng, columns=['Incomes electricity'], dtype='float')
def calculate(self) -> pd.DataFrame:
"""
Calculate total operational incomes
:return: pd.DataFrame
"""
building = self._building
archetype = self._archetype
if cte.YEAR not in building.onsite_electrical_production:
onsite_electricity_production = 0
else:
onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0]
for year in range(1, self._configuration.number_of_years + 1):
price_increase_electricity = math.pow(1 + self._configuration.electricity_price_index, year)
price_export = archetype.income.electricity_export # to account for unit change
self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
(onsite_electricity_production / 3.6e6) * price_export * price_increase_electricity
)
self._yearly_operational_incomes.fillna(0, inplace=True)
return self._yearly_operational_incomes

8
costing_package/version.py
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"""
Cost version number
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
"""
__version__ = '0.1.0.5'

147
energy_system_modelling_package/energy_system_modelling_factories/archetypes/montreal/archetype_cluster_1.py
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import csv
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
HeatPumpBoilerTesHeating
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_cooling import \
HeatPumpCooling
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.domestic_hot_water_heat_pump_with_tes import \
DomesticHotWaterHeatPumpTes
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_model import PVModel
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
class ArchetypeCluster1:
def __init__(self, building, dt, output_path, csv_output=True):
self.building = building
self.dt = dt
self.output_path = output_path
self.csv_output = csv_output
self.heating_results, self.building_heating_hourly_consumption = self.heating_system_simulation()
self.cooling_results, self.total_cooling_consumption_hourly = self.cooling_system_simulation()
self.dhw_results, self.total_dhw_consumption_hourly = self.dhw_system_simulation()
if 'PV' in self.building.energy_systems_archetype_name:
self.pv_results = self.pv_system_simulation()
else:
self.pv_results = None
def heating_system_simulation(self):
building_heating_hourly_consumption = []
boiler = self.building.energy_systems[1].generation_systems[0]
hp = self.building.energy_systems[1].generation_systems[1]
tes = self.building.energy_systems[1].generation_systems[0].energy_storage_systems[0]
heating_demand_joules = self.building.heating_demand[cte.HOUR]
heating_peak_load_watts = self.building.heating_peak_load[cte.YEAR][0]
upper_limit_tes_heating = 55
outdoor_temperature = self.building.external_temperature[cte.HOUR]
results = HeatPumpBoilerTesHeating(hp=hp,
boiler=boiler,
tes=tes,
hourly_heating_demand_joules=heating_demand_joules,
heating_peak_load_watts=heating_peak_load_watts,
upper_limit_tes=upper_limit_tes_heating,
outdoor_temperature=outdoor_temperature,
dt=self.dt).simulation()
number_of_ts = int(cte.HOUR_TO_SECONDS/self.dt)
heating_consumption_joules = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in
results['Total Heating Power Consumption (W)']]
heating_consumption = 0
for i in range(1, len(heating_consumption_joules)):
heating_consumption += heating_consumption_joules[i]
if (i - 1) % number_of_ts == 0:
building_heating_hourly_consumption.append(heating_consumption)
heating_consumption = 0
return results, building_heating_hourly_consumption
def cooling_system_simulation(self):
hp = self.building.energy_systems[2].generation_systems[0]
cooling_demand_joules = self.building.cooling_demand[cte.HOUR]
cooling_peak_load = self.building.cooling_peak_load[cte.YEAR][0]
cutoff_temperature = 11
outdoor_temperature = self.building.external_temperature[cte.HOUR]
results = HeatPumpCooling(hp=hp,
hourly_cooling_demand_joules=cooling_demand_joules,
cooling_peak_load_watts=cooling_peak_load,
cutoff_temperature=cutoff_temperature,
outdoor_temperature=outdoor_temperature,
dt=self.dt).simulation()
building_cooling_hourly_consumption = hp.energy_consumption[cte.COOLING][cte.HOUR]
return results, building_cooling_hourly_consumption
def dhw_system_simulation(self):
building_dhw_hourly_consumption = []
hp = self.building.energy_systems[-1].generation_systems[0]
tes = self.building.energy_systems[-1].generation_systems[0].energy_storage_systems[0]
dhw_demand_joules = self.building.domestic_hot_water_heat_demand[cte.HOUR]
upper_limit_tes = 65
outdoor_temperature = self.building.external_temperature[cte.HOUR]
results = DomesticHotWaterHeatPumpTes(hp=hp,
tes=tes,
hourly_dhw_demand_joules=dhw_demand_joules,
upper_limit_tes=upper_limit_tes,
outdoor_temperature=outdoor_temperature,
dt=self.dt).simulation()
number_of_ts = int(cte.HOUR_TO_SECONDS/self.dt)
dhw_consumption_joules = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in
results['Total DHW Power Consumption (W)']]
dhw_consumption = 0
for i in range(1, len(dhw_consumption_joules)):
dhw_consumption += dhw_consumption_joules[i]
if (i - 1) % number_of_ts == 0:
building_dhw_hourly_consumption.append(dhw_consumption)
dhw_consumption = 0
return results, building_dhw_hourly_consumption
def pv_system_simulation(self):
results = None
pv = self.building.energy_systems[0].generation_systems[0]
hourly_electricity_demand = HourlyElectricityDemand(self.building).calculate()
model_type = 'fixed_efficiency'
if model_type == 'fixed_efficiency':
results = PVModel(pv=pv,
hourly_electricity_demand_joules=hourly_electricity_demand,
solar_radiation=self.building.roofs[0].global_irradiance_tilted[cte.HOUR],
installed_pv_area=self.building.roofs[0].installed_solar_collector_area,
model_type='fixed_efficiency').fixed_efficiency()
return results
def enrich_building(self):
results = self.heating_results | self.cooling_results | self.dhw_results
self.building.heating_consumption[cte.HOUR] = self.building_heating_hourly_consumption
self.building.heating_consumption[cte.MONTH] = (
MonthlyValues.get_total_month(self.building.heating_consumption[cte.HOUR]))
self.building.heating_consumption[cte.YEAR] = [sum(self.building.heating_consumption[cte.MONTH])]
self.building.cooling_consumption[cte.HOUR] = self.total_cooling_consumption_hourly
self.building.cooling_consumption[cte.MONTH] = (
MonthlyValues.get_total_month(self.building.cooling_consumption[cte.HOUR]))
self.building.cooling_consumption[cte.YEAR] = [sum(self.building.cooling_consumption[cte.MONTH])]
self.building.domestic_hot_water_consumption[cte.HOUR] = self.total_dhw_consumption_hourly
self.building.domestic_hot_water_consumption[cte.MONTH] = (
MonthlyValues.get_total_month(self.building.domestic_hot_water_consumption[cte.HOUR]))
self.building.domestic_hot_water_consumption[cte.YEAR] = [
sum(self.building.domestic_hot_water_consumption[cte.MONTH])]
if self.pv_results is not None:
self.building.onsite_electrical_production[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
self.pv_results['PV Output (W)']]
self.building.onsite_electrical_production[cte.MONTH] = MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR])
self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]
if self.csv_output:
file_name = f'pv_system_simulation_results_{self.building.name}.csv'
with open(self.output_path / file_name, 'w', newline='') as csvfile:
output_file = csv.writer(csvfile)
# Write header
output_file.writerow(self.pv_results.keys())
# Write data
output_file.writerows(zip(*self.pv_results.values()))
if self.csv_output:
file_name = f'energy_system_simulation_results_{self.building.name}.csv'
with open(self.output_path / file_name, 'w', newline='') as csvfile:
output_file = csv.writer(csvfile)
# Write header
output_file.writerow(results.keys())
# Write data
output_file.writerows(zip(*results.values()))

79
energy_system_modelling_package/energy_system_modelling_factories/energy_system_sizing_factory.py
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"""
EnergySystemSizingSimulationFactory retrieve the energy system archetype sizing and simulation module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2024 Concordia CERC group
Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
"""
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.optimal_sizing import \
OptimalSizing
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.peak_load_sizing import \
PeakLoadSizing
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_sizing import PVSizing
class EnergySystemsSizingFactory:
"""
EnergySystemsFactory class
"""
def __init__(self, handler, city):
self._handler = '_' + handler.lower()
self._city = city
def _peak_load_sizing(self):
"""
Size Energy Systems based on a Load Matching method using the heating, cooling, and dhw peak loads
"""
PeakLoadSizing(self._city).enrich_buildings()
self._city.level_of_detail.energy_systems = 1
for building in self._city.buildings:
building.level_of_detail.energy_systems = 1
def _optimal_sizing(self):
"""
Size Energy Systems using a Single or Multi Objective GA
"""
OptimalSizing(self._city, optimization_scenario='cost_energy_consumption').enrich_buildings()
self._city.level_of_detail.energy_systems = 1
for building in self._city.buildings:
building.level_of_detail.energy_systems = 1
def _pv_sizing(self):
"""
Size rooftop, facade or mixture of them for buildings
"""
system_type = 'rooftop'
results = {}
if system_type == 'rooftop':
surface_azimuth = 180
maintenance_factor = 0.1
mechanical_equipment_factor = 0.3
orientation_factor = 0.1
tilt_angle = self._city.latitude
pv_sizing = PVSizing(self._city,
tilt_angle=tilt_angle,
surface_azimuth=surface_azimuth,
mechanical_equipment_factor=mechanical_equipment_factor,
maintenance_factor=maintenance_factor,
orientation_factor=orientation_factor,
system_type=system_type)
results = pv_sizing.rooftop_sizing()
pv_sizing.rooftop_tilted_radiation()
self._city.level_of_detail.energy_systems = 1
for building in self._city.buildings:
building.level_of_detail.energy_systems = 1
return results
def _district_heating_cooling_sizing(self):
"""
Size District Heating and Cooling Network
"""
pass
def enrich(self):
"""
Enrich the city given to the class using the class given handler
:return: None
"""
return getattr(self, self._handler, lambda: None)()

120
energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/domestic_hot_water_heat_pump_with_tes.py
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import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_characteristics import HeatPump
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.thermal_storage_tank import StorageTank
from hub.helpers.monthly_values import MonthlyValues
class DomesticHotWaterHeatPumpTes:
def __init__(self, hp, tes, hourly_dhw_demand_joules, upper_limit_tes,
outdoor_temperature, dt=None):
self.hp = hp
self.tes = tes
self.dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_dhw_demand_joules]
self.upper_limit_tes = upper_limit_tes
self.hp_characteristics = HeatPump(self.hp, outdoor_temperature)
self.t_out = outdoor_temperature
self.dt = dt
self.results = {}
def simulation(self):
hp = self.hp
tes = self.tes
heating_coil_nominal_output = 0
if tes.heating_coil_capacity is not None:
heating_coil_nominal_output = float(tes.heating_coil_capacity)
storage_tank = StorageTank(volume=float(tes.volume),
height=float(tes.height),
material_layers=tes.layers,
heating_coil_capacity=heating_coil_nominal_output)
hp_delta_t = 8
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
source_temperature_hourly = self.hp_characteristics.hp_source_temperature()
source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
demand = [0] + [x for x in self.dhw_demand for _ in range(number_of_ts)]
variable_names = ["t_sup_hp", "t_tank", "m_ch", "m_dis", "q_hp", "q_coil", "hp_cop",
"hp_electricity", "available hot water (m3)", "refill flow rate (kg/s)", "total_consumption"]
num_hours = len(demand)
variables = {name: [0] * num_hours for name in variable_names}
(t_sup_hp, t_tank, m_ch, m_dis, m_refill, q_hp, q_coil, hp_cop, hp_electricity, v_dhw, total_consumption) = \
[variables[name] for name in variable_names]
freshwater_temperature = 18
t_tank[0] = 65
for i in range(len(demand) - 1):
delta_t_demand = demand[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY *
storage_tank.volume))
if t_tank[i] < self.upper_limit_tes:
q_hp[i] = hp.nominal_heat_output
delta_t_hp = q_hp[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY * storage_tank.volume))
if demand[i] > 0:
dhw_needed = (demand[i] * cte.HOUR_TO_SECONDS) / (cte.WATER_HEAT_CAPACITY * t_tank[i] * cte.WATER_DENSITY)
m_dis[i] = dhw_needed * cte.WATER_DENSITY / cte.HOUR_TO_SECONDS
m_refill[i] = m_dis[i]
delta_t_freshwater = m_refill[i] * (t_tank[i] - freshwater_temperature) * (self.dt / (storage_tank.volume *
cte.WATER_DENSITY))
if t_tank[i] < 60:
q_coil[i] = float(storage_tank.heating_coil_capacity)
delta_t_coil = q_coil[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY * storage_tank.volume))
if q_hp[i] > 0:
m_ch[i] = q_hp[i] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
t_sup_hp[i] = (q_hp[i] / (m_ch[i] * cte.WATER_HEAT_CAPACITY)) + t_tank[i]
else:
m_ch[i] = 0
t_sup_hp[i] = t_tank[i]
if q_hp[i] > 0:
if hp.source_medium == cte.AIR and hp.supply_medium == cte.WATER:
hp_cop[i] = self.hp_characteristics.air_to_water_cop(source_temperature[i], t_tank[i],
mode=cte.DOMESTIC_HOT_WATER)
hp_electricity[i] = q_hp[i] / hp_cop[i]
else:
hp_cop[i] = 0
hp_electricity[i] = 0
t_tank[i + 1] = t_tank[i] + (delta_t_hp - delta_t_freshwater - delta_t_demand + delta_t_coil)
total_consumption[i] = hp_electricity[i] + q_coil[i]
tes.temperature = []
hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil]
hp_hourly = []
coil_hourly = []
coil_sum = 0
hp_sum = 0
for i in range(1, len(demand)):
hp_sum += hp_electricity_j[i]
coil_sum += heating_coil_j[i]
if (i - 1) % number_of_ts == 0:
tes.temperature.append(t_tank[i])
hp_hourly.append(hp_sum)
coil_hourly.append(coil_sum)
hp_sum = 0
coil_sum = 0
hp.energy_consumption[cte.DOMESTIC_HOT_WATER] = {}
hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR] = hp_hourly
if len(self.dhw_demand) == 8760:
hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
sum(hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
if self.tes.heating_coil_capacity is not None:
tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER] = {}
tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR] = coil_hourly
if len(self.dhw_demand) == 8760:
tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
sum(tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
self.results['DHW Demand (W)'] = demand
self.results['DHW HP Heat Output (W)'] = q_hp
self.results['DHW HP Electricity Consumption (W)'] = hp_electricity
self.results['DHW HP Source Temperature'] = source_temperature
self.results['DHW HP Supply Temperature'] = t_sup_hp
self.results['DHW HP COP'] = hp_cop
self.results['DHW TES Heating Coil Heat Output (W)'] = q_coil
self.results['DHW TES Temperature'] = t_tank
self.results['DHW TES Charging Flow Rate (kg/s)'] = m_ch
self.results['DHW Flow Rate (kg/s)'] = m_dis
self.results['DHW TES Refill Flow Rate (kg/s)'] = m_refill
self.results['Available Water in Tank (m3)'] = v_dhw
self.results['Total DHW Power Consumption (W)'] = total_consumption
return self.results

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energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_boiler_tes_heating.py
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import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_characteristics import \
HeatPump
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.thermal_storage_tank import \
StorageTank
class HeatPumpBoilerTesHeating:
def __init__(self, hp, boiler, tes, hourly_heating_demand_joules, heating_peak_load_watts, upper_limit_tes,
outdoor_temperature, dt=None):
self.hp = hp
self.boiler = boiler
self.tes = tes
self.heating_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_heating_demand_joules]
if heating_peak_load_watts is not None:
self.heating_peak_load = heating_peak_load_watts
else:
self.heating_peak_load = max(hourly_heating_demand_joules) / cte.HOUR_TO_SECONDS
self.upper_limit_tes = upper_limit_tes
self.hp_characteristics = HeatPump(self.hp, outdoor_temperature)
self.t_out = outdoor_temperature
self.dt = dt
self.results = {}
def simulation(self):
hp, boiler, tes = self.hp, self.boiler, self.tes
heating_coil_nominal_output = 0
if tes.heating_coil_capacity is not None:
heating_coil_nominal_output = float(tes.heating_coil_capacity)
storage_tank = StorageTank(volume=float(tes.volume),
height=float(tes.height),
material_layers=tes.layers,
heating_coil_capacity=heating_coil_nominal_output)
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
demand = [0] + [x for x in self.heating_demand for _ in range(number_of_ts)]
t_out = [0] + [x for x in self.t_out for _ in range(number_of_ts)]
source_temperature_hourly = self.hp_characteristics.hp_source_temperature()
source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
variable_names = ["t_sup_hp", "t_tank", "t_ret", "m_ch", "m_dis", "q_hp", "q_boiler", "hp_cop",
"hp_electricity", "boiler_gas_consumption", "t_sup_boiler", "boiler_energy_consumption",
"heating_consumption", "heating_coil_output", "total_heating_energy_consumption"]
num_hours = len(demand)
variables = {name: [0] * num_hours for name in variable_names}
(t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, q_boiler, hp_cop,
hp_electricity, boiler_fuel_consumption, t_sup_boiler, boiler_energy_consumption, heating_consumption, q_coil,
total_consumption) = [variables[name] for name in variable_names]
t_tank[0] = self.upper_limit_tes
hp_delta_t = 5
# storage temperature prediction
for i in range(len(demand) - 1):
t_tank[i + 1] = storage_tank.calculate_space_heating_fully_mixed(charging_flow_rate=m_ch[i],
discharging_flow_rate=m_dis[i],
supply_temperature=t_sup_boiler[i],
return_temperature=t_ret[i],
current_tank_temperature=t_tank[i],
heat_generator_input=q_coil[i],
ambient_temperature=t_out[i],
dt=self.dt)
# hp operation
if t_tank[i + 1] < 40:
q_hp[i + 1] = hp.nominal_heat_output
m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] == 0:
q_hp[i + 1] = 0
m_ch[i + 1] = 0
t_sup_hp[i + 1] = t_tank[i + 1]
elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] > 0:
q_hp[i + 1] = hp.nominal_heat_output
m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
else:
q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
if q_hp[i + 1] > 0:
if hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
hp_cop[i + 1] = self.hp_characteristics.air_to_water_cop(source_temperature[i + 1], t_tank[i + 1], mode=cte.HEATING)
hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
else:
hp_cop[i + 1] = 0
hp_electricity[i + 1] = 0
# boiler operation
if q_hp[i + 1] > 0:
if t_sup_hp[i + 1] < 45:
q_boiler[i + 1] = boiler.nominal_heat_output
elif demand[i + 1] > 0.5 * self.heating_peak_load / self.dt:
q_boiler[i + 1] = 0.5 * boiler.nominal_heat_output
boiler_energy_consumption[i + 1] = q_boiler[i + 1] / float(boiler.heat_efficiency)
if boiler.fuel_type == cte.ELECTRICITY:
boiler_fuel_consumption[i + 1] = boiler_energy_consumption[i + 1]
else:
# TODO: Other fuels should be considered
boiler_fuel_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (
float(boiler.heat_efficiency) * cte.NATURAL_GAS_LHV)
t_sup_boiler[i + 1] = t_sup_hp[i + 1] + (q_boiler[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY))
# heating coil operation
if t_tank[i + 1] < 35:
q_coil[i + 1] = heating_coil_nominal_output
# storage discharging
if demand[i + 1] == 0:
m_dis[i + 1] = 0
t_ret[i + 1] = t_tank[i + 1]
else:
if demand[i + 1] > 0.5 * self.heating_peak_load:
factor = 8
else:
factor = 4
m_dis[i + 1] = self.heating_peak_load / (cte.WATER_HEAT_CAPACITY * factor)
t_ret[i + 1] = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY)
# total consumption
total_consumption[i + 1] = hp_electricity[i + 1] + boiler_energy_consumption[i + 1] + q_coil[i + 1]
tes.temperature = []
hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
boiler_consumption_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in boiler_energy_consumption]
heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil]
hp_hourly = []
boiler_hourly = []
coil_hourly = []
boiler_sum = 0
hp_sum = 0
coil_sum = 0
for i in range(1, len(demand)):
hp_sum += hp_electricity_j[i]
boiler_sum += boiler_consumption_j[i]
coil_sum += heating_coil_j[i]
if (i - 1) % number_of_ts == 0:
tes.temperature.append(t_tank[i])
hp_hourly.append(hp_sum)
boiler_hourly.append(boiler_sum)
coil_hourly.append(coil_sum)
hp_sum = 0
boiler_sum = 0
coil_sum = 0
hp.energy_consumption[cte.HEATING] = {}
hp.energy_consumption[cte.HEATING][cte.HOUR] = hp_hourly
boiler.energy_consumption[cte.HEATING] = {}
boiler.energy_consumption[cte.HEATING][cte.HOUR] = boiler_hourly
if len(self.heating_demand) == 8760:
hp.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
hp.energy_consumption[cte.HEATING][cte.HOUR])
hp.energy_consumption[cte.HEATING][cte.YEAR] = [
sum(hp.energy_consumption[cte.HEATING][cte.MONTH])]
boiler.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
boiler.energy_consumption[cte.HEATING][cte.HOUR])
boiler.energy_consumption[cte.HEATING][cte.YEAR] = [
sum(boiler.energy_consumption[cte.HEATING][cte.MONTH])]
if tes.heating_coil_capacity is not None:
tes.heating_coil_energy_consumption[cte.HEATING] = {}
if len(self.heating_demand) == 8760:
tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR] = coil_hourly
tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR])
tes.heating_coil_energy_consumption[cte.HEATING][cte.YEAR] = [
sum(tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH])]
self.results['Heating Demand (W)'] = demand
self.results['HP Heat Output (W)'] = q_hp
self.results['HP Source Temperature'] = source_temperature
self.results['HP Supply Temperature'] = t_sup_hp
self.results['HP COP'] = hp_cop
self.results['HP Electricity Consumption (W)'] = hp_electricity
self.results['Boiler Heat Output (W)'] = q_boiler
self.results['Boiler Power Consumption (W)'] = boiler_energy_consumption
self.results['Boiler Supply Temperature'] = t_sup_boiler
self.results['Boiler Fuel Consumption'] = boiler_fuel_consumption
self.results['TES Temperature'] = t_tank
self.results['Heating Coil heat input'] = q_coil
self.results['TES Charging Flow Rate (kg/s)'] = m_ch
self.results['TES Discharge Flow Rate (kg/s)'] = m_dis
self.results['Heating Loop Return Temperature'] = t_ret
self.results['Total Heating Power Consumption (W)'] = total_consumption
return self.results

54
energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_characteristics.py
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import hub.helpers.constants as cte
class HeatPump:
def __init__(self, hp, t_out):
self.hp = hp
self.t_out = t_out
def hp_source_temperature(self):
if self.hp.source_medium == cte.AIR:
self.hp.source_temperature = self.t_out
elif self.hp.source_medium == cte.GROUND:
average_air_temperature = sum(self.t_out) / len(self.t_out)
self.hp.source_temperature = [average_air_temperature + 10] * len(self.t_out)
elif self.hp.source_medium == cte.WATER:
self.hp.source_temperature = [15] * len(self.t_out)
return self.hp.source_temperature
def air_to_water_cop(self, source_temperature, inlet_water_temperature, mode=cte.HEATING):
cop_coefficient = 1
t_inlet_water_fahrenheit = 1.8 * inlet_water_temperature + 32
t_source_fahrenheit = 1.8 * source_temperature + 32
if mode == cte.HEATING:
if self.hp.heat_efficiency_curve is not None:
cop_curve_coefficients = [float(coefficient) for coefficient in self.hp.heat_efficiency_curve.coefficients]
cop_coefficient = (1 / (cop_curve_coefficients[0] +
cop_curve_coefficients[1] * t_inlet_water_fahrenheit +
cop_curve_coefficients[2] * t_inlet_water_fahrenheit ** 2 +
cop_curve_coefficients[3] * t_source_fahrenheit +
cop_curve_coefficients[4] * t_source_fahrenheit ** 2 +
cop_curve_coefficients[5] * t_inlet_water_fahrenheit * t_source_fahrenheit))
hp_efficiency = float(self.hp.heat_efficiency)
elif mode == cte.COOLING:
if self.hp.cooling_efficiency_curve is not None:
cop_curve_coefficients = [float(coefficient) for coefficient in self.hp.cooling_efficiency_curve.coefficients]
cop_coefficient = (1 / (cop_curve_coefficients[0] +
cop_curve_coefficients[1] * t_inlet_water_fahrenheit +
cop_curve_coefficients[2] * t_inlet_water_fahrenheit ** 2 +
cop_curve_coefficients[3] * t_source_fahrenheit +
cop_curve_coefficients[4] * t_source_fahrenheit ** 2 +
cop_curve_coefficients[5] * t_inlet_water_fahrenheit * t_source_fahrenheit)) / 3.41214
hp_efficiency = float(self.hp.cooling_efficiency)
else:
if self.hp.heat_efficiency_curve is not None:
cop_curve_coefficients = [float(coefficient) for coefficient in self.hp.heat_efficiency_curve.coefficients]
cop_coefficient = (cop_curve_coefficients[0] +
cop_curve_coefficients[1] * source_temperature +
cop_curve_coefficients[2] * source_temperature ** 2 +
cop_curve_coefficients[3] * inlet_water_temperature +
cop_curve_coefficients[4] * inlet_water_temperature ** 2 +
cop_curve_coefficients[5] * inlet_water_temperature * source_temperature)
hp_efficiency = float(self.hp.heat_efficiency)
hp_cop = cop_coefficient * hp_efficiency
return hp_cop

89
energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_cooling.py
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import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_characteristics import HeatPump
class HeatPumpCooling:
def __init__(self, hp, hourly_cooling_demand_joules, cooling_peak_load_watts, cutoff_temperature, outdoor_temperature,
dt=900):
self.hp = hp
self.cooling_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_cooling_demand_joules]
self.cooling_peak_load = cooling_peak_load_watts
self.cutoff_temperature = cutoff_temperature
self.dt = dt
self.results = {}
self.heat_pump_characteristics = HeatPump(self.hp, outdoor_temperature)
def simulation(self):
source_temperature_hourly = self.heat_pump_characteristics.hp_source_temperature()
cooling_efficiency = float(self.hp.cooling_efficiency)
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
demand = [0] + [x for x in self.cooling_demand for _ in range(number_of_ts)]
source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
variable_names = ["t_sup_hp", "t_ret", "m", "q_hp", "hp_electricity", "hp_cop"]
num_hours = len(demand)
variables = {name: [0] * num_hours for name in variable_names}
(t_sup_hp, t_ret, m, q_hp, hp_electricity, hp_cop) = [variables[name] for name in variable_names]
t_ret[0] = self.cutoff_temperature
for i in range(1, len(demand)):
if demand[i] > 0.15 * self.cooling_peak_load:
m[i] = self.hp.nominal_cooling_output / (cte.WATER_HEAT_CAPACITY * 5)
if t_ret[i - 1] >= self.cutoff_temperature:
if demand[i] < 0.25 * self.cooling_peak_load:
q_hp[i] = 0.25 * self.hp.nominal_cooling_output
elif demand[i] < 0.5 * self.cooling_peak_load:
q_hp[i] = 0.5 * self.hp.nominal_cooling_output
else:
q_hp[i] = self.hp.nominal_cooling_output
t_sup_hp[i] = t_ret[i - 1] - q_hp[i] / (m[i] * cte.WATER_HEAT_CAPACITY)
else:
q_hp[i] = 0
t_sup_hp[i] = t_ret[i - 1]
if m[i] == 0:
t_ret[i] = t_sup_hp[i]
else:
t_ret[i] = t_sup_hp[i] + demand[i] / (m[i] * cte.WATER_HEAT_CAPACITY)
else:
m[i] = 0
q_hp[i] = 0
t_sup_hp[i] = t_ret[i - 1]
t_ret[i] = t_ret[i - 1]
if q_hp[i] > 0:
if self.hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
hp_cop[i] = self.heat_pump_characteristics.air_to_water_cop(source_temperature[i], t_ret[i],
mode=cte.COOLING)
hp_electricity[i] = q_hp[i] / hp_cop[i]
else:
hp_cop[i] = 0
hp_electricity[i] = 0
hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
hp_hourly = []
hp_supply_temperature_hourly = []
hp_sum = 0
for i in range(1, len(demand)):
hp_sum += hp_electricity_j[i]
if (i - 1) % number_of_ts == 0:
hp_hourly.append(hp_sum)
hp_supply_temperature_hourly.append(t_sup_hp[i])
hp_sum = 0
self.hp.cooling_supply_temperature = hp_supply_temperature_hourly
self.hp.energy_consumption[cte.COOLING] = {}
self.hp.energy_consumption[cte.COOLING][cte.HOUR] = hp_hourly
self.hp.energy_consumption[cte.COOLING][cte.MONTH] = MonthlyValues.get_total_month(
self.hp.energy_consumption[cte.COOLING][cte.HOUR])
self.hp.energy_consumption[cte.COOLING][cte.YEAR] = [
sum(self.hp.energy_consumption[cte.COOLING][cte.MONTH])]
self.results['Cooling Demand (W)'] = demand
self.results['HP Cooling Output (W)'] = q_hp
self.results['HP Cooling Source Temperature'] = source_temperature
self.results['HP Cooling Supply Temperature'] = t_sup_hp
self.results['HP Cooling COP'] = hp_cop
self.results['HP Electricity Consumption'] = hp_electricity
self.results['Cooling Loop Flow Rate (kg/s)'] = m
self.results['Cooling Loop Return Temperature'] = t_ret
return self.results

47
energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/thermal_storage_tank.py
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import math
import hub.helpers.constants as cte
class StorageTank:
"""
Calculation of the temperature inside a hot water storage tank in the next time step
"""
def __init__(self, volume, height, material_layers, heating_coil_capacity, stratification_layer=1):
self.volume = volume
self.height = height
self.materials = material_layers
self.number_of_vertical_layers = stratification_layer
self.heating_coil_capacity = heating_coil_capacity
def heat_loss_coefficient(self):
r_tot = sum(float(layer.thickness) / float(layer.material.conductivity) for layer in
self.materials)
u_tot = 1 / r_tot
d = math.sqrt((4 * self.volume) / (math.pi * self.height))
a_side = math.pi * d * self.height
a_top = math.pi * d ** 2 / 4
if self.number_of_vertical_layers == 1:
ua = u_tot * (2 * a_top + a_side)
return ua
else:
ua_side = u_tot * a_side
ua_top_bottom = u_tot * (a_top + a_side)
return ua_side, ua_top_bottom
def calculate_space_heating_fully_mixed(self, charging_flow_rate, discharging_flow_rate, supply_temperature,
return_temperature,
current_tank_temperature, heat_generator_input, ambient_temperature, dt):
ua = self.heat_loss_coefficient()
t_tank = (current_tank_temperature +
(charging_flow_rate * (supply_temperature - current_tank_temperature) +
(ua * (ambient_temperature - current_tank_temperature)) / cte.WATER_HEAT_CAPACITY -
discharging_flow_rate * (current_tank_temperature - return_temperature) +
heat_generator_input / cte.WATER_HEAT_CAPACITY) * (dt / (cte.WATER_DENSITY * self.volume)))
return t_tank
def calculate_dhw_fully_mixed(self, charging_flow_rate, discharging_flow_rate, supply_temperature, return_temperature,
current_tank_temperature, heat_generator_input, ambient_temperature, dt):
pass

36
energy_system_modelling_package/energy_system_modelling_factories/montreal_energy_system_archetype_modelling_factory.py
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@ -1,36 +0,0 @@
"""
EnergySystemSizingSimulationFactory retrieve the energy system archetype sizing and simulation module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2024 Concordia CERC group
Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
"""
from energy_system_modelling_package.energy_system_modelling_factories.archetypes.montreal.archetype_cluster_1 import ArchetypeCluster1
class MontrealEnergySystemArchetypesSimulationFactory:
"""
EnergySystemsFactory class
"""
def __init__(self, handler, building, output_path, csv_output=True):
self._output_path = output_path
self._handler = '_' + handler.lower()
self._building = building
self._csv_output = csv_output
def _archetype_cluster_1(self):
"""
Enrich the city by using the sizing and simulation model developed for archetype13 of montreal_future_systems
"""
dt = 900
ArchetypeCluster1(self._building, dt, self._output_path, self._csv_output).enrich_building()
self._building.level_of_detail.energy_systems = 2
self._building.level_of_detail.energy_systems = 2
def enrich(self):
"""
Enrich the city given to the class using the class given handler
:return: None
"""
getattr(self, self._handler, lambda: None)()

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/electricity_demand_calculator.py
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import hub.helpers.constants as cte
class HourlyElectricityDemand:
def __init__(self, building):
self.building = building
def calculate(self):
hourly_electricity_consumption = []
energy_systems = self.building.energy_systems
appliance = self.building.appliances_electrical_demand[cte.HOUR]
lighting = self.building.lighting_electrical_demand[cte.HOUR]
elec_heating = 0
elec_cooling = 0
elec_dhw = 0
if cte.HEATING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_heating = 1
if cte.COOLING in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_cooling = 1
if cte.DOMESTIC_HOT_WATER in self.building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_dhw = 1
heating = None
cooling = None
dhw = None
if elec_heating == 1:
for energy_system in energy_systems:
if cte.HEATING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if cte.HEATING in generation_system.energy_consumption:
heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
heating = [x / 2 for x in self.building.heating_consumption[cte.HOUR]]
else:
heating = self.building.heating_consumption[cte.HOUR]
if elec_dhw == 1:
for energy_system in energy_systems:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
dhw = [x / 2 for x in self.building.domestic_hot_water_consumption[cte.HOUR]]
else:
dhw = self.building.domestic_hot_water_consumption[cte.HOUR]
if elec_cooling == 1:
for energy_system in energy_systems:
if cte.COOLING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if cte.COOLING in generation_system.energy_consumption:
cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
cooling = [x / 2 for x in self.building.cooling_consumption[cte.HOUR]]
else:
cooling = self.building.cooling_consumption[cte.HOUR]
for i in range(len(self.building.heating_demand[cte.HOUR])):
hourly = 0
hourly += appliance[i]
hourly += lighting[i]
if heating is not None:
hourly += heating[i]
if cooling is not None:
hourly += cooling[i]
if dhw is not None:
hourly += dhw[i]
hourly_electricity_consumption.append(hourly)
return hourly_electricity_consumption

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_feasibility.py
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from pathlib import Path
import subprocess
from hub.imports.geometry_factory import GeometryFactory
from building_modelling.geojson_creator import process_geojson
from hub.helpers.dictionaries import Dictionaries
from hub.imports.weather_factory import WeatherFactory
from hub.imports.results_factory import ResultFactory
from hub.exports.exports_factory import ExportsFactory
def pv_feasibility(current_x, current_y, current_diff, selected_buildings):
input_files_path = (Path(__file__).parent.parent.parent.parent / 'input_files')
output_path = (Path(__file__).parent.parent.parent.parent / 'out_files').resolve()
sra_output_path = output_path / 'sra_outputs' / 'extended_city_sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
new_diff = current_diff * 5
geojson_file = process_geojson(x=current_x, y=current_y, diff=new_diff, expansion=True)
file_path = input_files_path / 'output_buildings.geojson'
city = GeometryFactory('geojson',
path=file_path,
height_field='height',
year_of_construction_field='year_of_construction',
function_field='function',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
WeatherFactory('epw', city).enrich()
ExportsFactory('sra', city, sra_output_path).export()
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
ResultFactory('sra', city, sra_output_path).enrich()
for selected_building in selected_buildings:
for building in city.buildings:
if selected_building.name == building.name:
selected_building.roofs[0].global_irradiance = building.roofs[0].global_irradiance

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_model.py
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import math
import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
class PVModel:
def __init__(self, pv, hourly_electricity_demand_joules, solar_radiation, installed_pv_area, model_type, ns=None,
np=None):
self.pv = pv
self.hourly_electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_electricity_demand_joules]
self.solar_radiation = solar_radiation
self.installed_pv_area = installed_pv_area
self._model_type = '_' + model_type.lower()
self.ns = ns
self.np = np
self.results = {}
def fixed_efficiency(self):
module_efficiency = float(self.pv.electricity_efficiency)
variable_names = ["pv_output", "import", "export", "self_sufficiency_ratio"]
variables = {name: [0] * len(self.hourly_electricity_demand) for name in variable_names}
(pv_out, grid_import, grid_export, self_sufficiency_ratio) = [variables[name] for name in variable_names]
for i in range(len(self.hourly_electricity_demand)):
pv_out[i] = module_efficiency * self.installed_pv_area * self.solar_radiation[i] / cte.WATTS_HOUR_TO_JULES
if pv_out[i] < self.hourly_electricity_demand[i]:
grid_import[i] = self.hourly_electricity_demand[i] - pv_out[i]
else:
grid_export[i] = pv_out[i] - self.hourly_electricity_demand[i]
self_sufficiency_ratio[i] = pv_out[i] / self.hourly_electricity_demand[i]
self.results['Electricity Demand (W)'] = self.hourly_electricity_demand
self.results['PV Output (W)'] = pv_out
self.results['Imported from Grid (W)'] = grid_import
self.results['Exported to Grid (W)'] = grid_export
self.results['Self Sufficiency Ratio'] = self_sufficiency_ratio
return self.results
def enrich(self):
"""
Enrich the city given to the class using the class given handler
:return: None
"""
return getattr(self, self._model_type, lambda: None)()

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_sizing.py
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import math
import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_angles import CitySolarAngles
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.radiation_tilted import RadiationTilted
class PVSizing(CitySolarAngles):
def __init__(self, city, tilt_angle, surface_azimuth=180, maintenance_factor=0.1, mechanical_equipment_factor=0.3,
orientation_factor=0.1, system_type='rooftop'):
super().__init__(location_latitude=city.latitude,
location_longitude=city.longitude,
tilt_angle=tilt_angle,
surface_azimuth_angle=surface_azimuth)
self.city = city
self.maintenance_factor = maintenance_factor
self.mechanical_equipment_factor = mechanical_equipment_factor
self.orientation_factor = orientation_factor
self.angles = self.calculate
self.system_type = system_type
def rooftop_sizing(self):
results = {}
# Available Roof Area
for building in self.city.buildings:
for energy_system in building.energy_systems:
for generation_system in energy_system.generation_systems:
if generation_system.system_type == cte.PHOTOVOLTAIC:
module_width = float(generation_system.width)
module_height = float(generation_system.height)
roof_area = 0
for roof in building.roofs:
roof_area += roof.perimeter_area
pv_module_area = module_width * module_height
available_roof = ((self.maintenance_factor + self.orientation_factor + self.mechanical_equipment_factor) *
roof_area)
# Inter-Row Spacing
winter_solstice = self.angles[(self.angles['AST'].dt.month == 12) &
(self.angles['AST'].dt.day == 21) &
(self.angles['AST'].dt.hour == 12)]
solar_altitude = winter_solstice['solar altitude'].values[0]
solar_azimuth = winter_solstice['solar azimuth'].values[0]
distance = ((module_height * abs(math.cos(math.radians(solar_azimuth)))) /
math.tan(math.radians(solar_altitude)))
distance = float(format(distance, '.1f'))
# Calculation of the number of panels
space_dimension = math.sqrt(available_roof)
space_dimension = float(format(space_dimension, '.2f'))
panels_per_row = math.ceil(space_dimension / module_width)
number_of_rows = math.ceil(space_dimension / distance)
total_number_of_panels = panels_per_row * number_of_rows
total_pv_area = panels_per_row * number_of_rows * pv_module_area
building.roofs[0].installed_solar_collector_area = total_pv_area
results[f'Building {building.name}'] = {'total_roof_area': roof_area,
'PV dedicated area': available_roof,
'total_pv_area': total_pv_area,
'total_number_of_panels': total_number_of_panels,
'number_of_rows': number_of_rows,
'panels_per_row': panels_per_row}
return results
def rooftop_tilted_radiation(self):
for building in self.city.buildings:
RadiationTilted(building=building,
solar_angles=self.angles,
tilt_angle=self.tilt_angle,
ghi=building.roofs[0].global_irradiance[cte.HOUR],
).enrich()
def facade_sizing(self):
pass

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_sizing_and_simulation.py
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import math
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.radiation_tilted import RadiationTilted
import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
class PVSizingSimulation(RadiationTilted):
def __init__(self, building, solar_angles, tilt_angle, module_height, module_width, ghi):
super().__init__(building, solar_angles, tilt_angle, ghi)
self.module_height = module_height
self.module_width = module_width
self.total_number_of_panels = 0
self.enrich()
def available_space(self):
roof_area = self.building.roofs[0].perimeter_area
maintenance_factor = 0.1
orientation_factor = 0.2
if self.building.function == cte.RESIDENTIAL:
mechanical_equipment_factor = 0.2
else:
mechanical_equipment_factor = 0.3
available_roof = (maintenance_factor + orientation_factor + mechanical_equipment_factor) * roof_area
return available_roof
def inter_row_spacing(self):
winter_solstice = self.df[(self.df['AST'].dt.month == 12) &
(self.df['AST'].dt.day == 21) &
(self.df['AST'].dt.hour == 12)]
solar_altitude = winter_solstice['solar altitude'].values[0]
solar_azimuth = winter_solstice['solar azimuth'].values[0]
distance = ((self.module_height * abs(math.cos(math.radians(solar_azimuth)))) /
math.tan(math.radians(solar_altitude)))
distance = float(format(distance, '.1f'))
return distance
def number_of_panels(self, available_roof, inter_row_distance):
space_dimension = math.sqrt(available_roof)
space_dimension = float(format(space_dimension, '.2f'))
panels_per_row = math.ceil(space_dimension / self.module_width)
number_of_rows = math.ceil(space_dimension / inter_row_distance)
self.total_number_of_panels = panels_per_row * number_of_rows
return panels_per_row, number_of_rows
def pv_output_constant_efficiency(self):
radiation = self.total_radiation_tilted
pv_module_area = self.module_width * self.module_height
available_roof = self.available_space()
inter_row_spacing = self.inter_row_spacing()
self.number_of_panels(available_roof, inter_row_spacing)
self.building.roofs[0].installed_solar_collector_area = pv_module_area * self.total_number_of_panels
system_efficiency = 0.2
pv_hourly_production = [x * system_efficiency * self.total_number_of_panels * pv_module_area *
cte.WATTS_HOUR_TO_JULES for x in radiation]
self.building.onsite_electrical_production[cte.HOUR] = pv_hourly_production
self.building.onsite_electrical_production[cte.MONTH] = (
MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR]))
self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/radiation_tilted.py
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import pandas as pd
import math
import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
class RadiationTilted:
def __init__(self, building, solar_angles, tilt_angle, ghi, solar_constant=1366.1, maximum_clearness_index=1,
min_cos_zenith=0.065, maximum_zenith_angle=87):
self.building = building
self.ghi = ghi
self.tilt_angle = tilt_angle
self.zeniths = solar_angles['zenith'].tolist()[:-1]
self.incidents = solar_angles['incident angle'].tolist()[:-1]
self.date_time = solar_angles['DateTime'].tolist()[:-1]
self.ast = solar_angles['AST'].tolist()[:-1]
self.solar_azimuth = solar_angles['solar azimuth'].tolist()[:-1]
self.solar_altitude = solar_angles['solar altitude'].tolist()[:-1]
data = {'DateTime': self.date_time, 'AST': self.ast, 'solar altitude': self.solar_altitude, 'zenith': self.zeniths,
'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents, 'ghi': self.ghi}
self.df = pd.DataFrame(data)
self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
self.df['AST'] = pd.to_datetime(self.df['AST'])
self.df.set_index('DateTime', inplace=True)
self.solar_constant = solar_constant
self.maximum_clearness_index = maximum_clearness_index
self.min_cos_zenith = min_cos_zenith
self.maximum_zenith_angle = maximum_zenith_angle
self.i_on = []
self.i_oh = []
self.k_t = []
self.fraction_diffuse = []
self.diffuse_horizontal = []
self.beam_horizontal = []
self.dni = []
self.beam_tilted = []
self.diffuse_tilted = []
self.total_radiation_tilted = []
self.calculate()
def dni_extra(self):
for i in range(len(self.df)):
self.i_on.append(self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * self.df.index.dayofyear[i] / 365))))
self.df['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
def clearness_index(self):
for i in range(len(self.df)):
self.i_oh.append(self.i_on[i] * max(math.cos(math.radians(self.zeniths[i])), self.min_cos_zenith))
self.k_t.append(self.ghi[i] / self.i_oh[i])
self.k_t[i] = max(0, self.k_t[i])
self.k_t[i] = min(self.maximum_clearness_index, self.k_t[i])
self.df['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
self.df['clearness index'] = self.k_t
def diffuse_fraction(self):
for i in range(len(self.df)):
if self.k_t[i] <= 0.22:
self.fraction_diffuse.append(1 - 0.09 * self.k_t[i])
elif self.k_t[i] <= 0.8:
self.fraction_diffuse.append(0.9511 - 0.1604 * self.k_t[i] + 4.388 * self.k_t[i] ** 2 -
16.638 * self.k_t[i] ** 3 + 12.336 * self.k_t[i] ** 4)
else:
self.fraction_diffuse.append(0.165)
if self.zeniths[i] > self.maximum_zenith_angle:
self.fraction_diffuse[i] = 1
self.df['diffuse fraction'] = self.fraction_diffuse
def radiation_components_horizontal(self):
for i in range(len(self.df)):
self.diffuse_horizontal.append(self.ghi[i] * self.fraction_diffuse[i])
self.beam_horizontal.append(self.ghi[i] - self.diffuse_horizontal[i])
self.dni.append((self.ghi[i] - self.diffuse_horizontal[i]) / math.cos(math.radians(self.zeniths[i])))
if self.zeniths[i] > self.maximum_zenith_angle or self.dni[i] < 0:
self.dni[i] = 0
self.df['diffuse horizontal (Wh/m2)'] = self.diffuse_horizontal
self.df['dni (Wh/m2)'] = self.dni
self.df['beam horizontal (Wh/m2)'] = self.beam_horizontal
def radiation_components_tilted(self):
for i in range(len(self.df)):
self.beam_tilted.append(self.dni[i] * math.cos(math.radians(self.incidents[i])))
self.beam_tilted[i] = max(self.beam_tilted[i], 0)
self.diffuse_tilted.append(self.diffuse_horizontal[i] * ((1 + math.cos(math.radians(self.tilt_angle))) / 2))
self.total_radiation_tilted.append(self.beam_tilted[i] + self.diffuse_tilted[i])
self.df['beam tilted (Wh/m2)'] = self.beam_tilted
self.df['diffuse tilted (Wh/m2)'] = self.diffuse_tilted
self.df['total radiation tilted (Wh/m2)'] = self.total_radiation_tilted
def calculate(self) -> pd.DataFrame:
self.dni_extra()
self.clearness_index()
self.diffuse_fraction()
self.radiation_components_horizontal()
self.radiation_components_tilted()
return self.df
def enrich(self):
tilted_radiation = self.total_radiation_tilted
self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = tilted_radiation
self.building.roofs[0].global_irradiance_tilted[cte.MONTH] = (
MonthlyValues.get_total_month(self.building.roofs[0].global_irradiance_tilted[cte.HOUR]))
self.building.roofs[0].global_irradiance_tilted[cte.YEAR] = \
[sum(self.building.roofs[0].global_irradiance_tilted[cte.MONTH])]

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energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_angles.py
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import math
import pandas as pd
from datetime import datetime
from pathlib import Path
class CitySolarAngles:
def __init__(self, location_latitude, location_longitude, tilt_angle, surface_azimuth_angle,
standard_meridian=-75):
self.location_latitude = location_latitude
self.location_longitude = location_longitude
self.location_latitude_rad = math.radians(location_latitude)
self.surface_azimuth_angle = surface_azimuth_angle
self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
self.tilt_angle = tilt_angle
self.tilt_angle_rad = math.radians(tilt_angle)
self.standard_meridian = standard_meridian
self.longitude_correction = (location_longitude - standard_meridian) * 4
self.timezone = 'Etc/GMT+5'
self.eot = []
self.ast = []
self.hour_angles = []
self.declinations = []
self.solar_altitudes = []
self.solar_azimuths = []
self.zeniths = []
self.incidents = []
self.beam_tilted = []
self.factor = []
self.times = pd.date_range(start='2023-01-01', end='2024-01-01', freq='h', tz=self.timezone)
self.df = pd.DataFrame(index=self.times)
self.day_of_year = self.df.index.dayofyear
def solar_time(self, datetime_val, day_of_year):
b = (day_of_year - 81) * 2 * math.pi / 364
eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
self.eot.append(eot)
# Calculate Local Solar Time (LST)
lst_hour = datetime_val.hour
lst_minute = datetime_val.minute
lst_second = datetime_val.second
lst = lst_hour + lst_minute / 60 + lst_second / 3600
# Calculate Apparent Solar Time (AST) in decimal hours
ast_decimal = lst + eot / 60 + self.longitude_correction / 60
ast_hours = int(ast_decimal)
ast_minutes = round((ast_decimal - ast_hours) * 60)
# Ensure ast_minutes is within valid range
if ast_minutes == 60:
ast_hours += 1
ast_minutes = 0
elif ast_minutes < 0:
ast_minutes = 0
ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
hour=ast_hours, minute=ast_minutes)
self.ast.append(ast_time)
return ast_time
def declination_angle(self, day_of_year):
declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
declination_radian = math.radians(declination)
self.declinations.append(declination)
return declination_radian
def hour_angle(self, ast_time):
hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
hour_angle_radian = math.radians(hour_angle)
self.hour_angles.append(hour_angle)
return hour_angle_radian
def solar_altitude(self, declination_radian, hour_angle_radian):
solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
math.sin(declination_radian))
solar_altitude = math.degrees(solar_altitude_radians)
self.solar_altitudes.append(solar_altitude)
return solar_altitude_radians
def zenith(self, solar_altitude_radians):
solar_altitude = math.degrees(solar_altitude_radians)
zenith_degree = 90 - solar_altitude
zenith_radian = math.radians(zenith_degree)
self.zeniths.append(zenith_degree)
return zenith_radian
def solar_azimuth_analytical(self, hourangle, declination, zenith):
numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
if math.isclose(denom, 0.0, abs_tol=1e-8):
cos_azi = 1.0
else:
cos_azi = numer / denom
cos_azi = max(-1.0, min(1.0, cos_azi))
sign_ha = math.copysign(1, hourangle)
solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
self.solar_azimuths.append(solar_azimuth_degrees)
return solar_azimuth_radians
def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
incident_radian = math.acos(math.cos(solar_altitude_radians) *
math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
math.cos(self.tilt_angle_rad))
incident_angle_degrees = math.degrees(incident_radian)
self.incidents.append(incident_angle_degrees)
return incident_radian
@property
def calculate(self) -> pd.DataFrame:
for i in range(len(self.times)):
datetime_val = self.times[i]
day_of_year = self.day_of_year[i]
declination_radians = self.declination_angle(day_of_year)
ast_time = self.solar_time(datetime_val, day_of_year)
hour_angle_radians = self.hour_angle(ast_time)
solar_altitude_radians = self.solar_altitude(declination_radians, hour_angle_radians)
zenith_radians = self.zenith(solar_altitude_radians)
solar_azimuth_radians = self.solar_azimuth_analytical(hour_angle_radians, declination_radians, zenith_radians)
incident_angle_radian = self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
self.df['DateTime'] = self.times
self.df['AST'] = self.ast
self.df['hour angle'] = self.hour_angles
self.df['eot'] = self.eot
self.df['declination angle'] = self.declinations
self.df['solar altitude'] = self.solar_altitudes
self.df['zenith'] = self.zeniths
self.df['solar azimuth'] = self.solar_azimuths
self.df['incident angle'] = self.incidents
return self.df

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energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/individual.py
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@ -1,470 +0,0 @@
import math
import random
from hub.helpers.dictionaries import Dictionaries
from hub.catalog_factories.costs_catalog_factory import CostsCatalogFactory
import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.domestic_hot_water_heat_pump_with_tes import \
DomesticHotWaterHeatPumpTes
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
HeatPumpBoilerTesHeating
import numpy_financial as npf
class Individual:
def __init__(self, building, energy_system, design_period_energy_demands, optimization_scenario,
heating_design_load=None, cooling_design_load=None, dt=900, fuel_price_index=0.05,
electricity_tariff_type='fixed', consumer_price_index=0.04, interest_rate=0.04,
discount_rate=0.03, percentage_credit=0, credit_years=15):
"""
:param building: building object
:param energy_system: energy system to be optimized
:param design_period_energy_demands: A dictionary of design period heating, cooling and dhw demands. Design period
is the day with the highest total demand and the two days before and after it
:param optimization_scenario: a string indicating the objective function from minimization of cost,
energy consumption, and both together
:param heating_design_load: heating design load in W
:param cooling_design_load: cooling design load in W
:param dt the time step size used for simulations
:param fuel_price_index the price increase index of all fuels. A single value is used for all fuels.
:param electricity_tariff_type the electricity tariff type between 'fixed' and 'variable' for economic optimization
:param consumer_price_index
"""
self.building = building
self.energy_system = energy_system
self.design_period_energy_demands = design_period_energy_demands
self.demand_types = energy_system.demand_types
self.optimization_scenario = optimization_scenario
self.heating_design_load = heating_design_load
self.cooling_design_load = cooling_design_load
self.available_space = building.volume / building.storeys_above_ground
self.dt = dt
self.fuel_price_index = fuel_price_index
self.electricity_tariff_type = electricity_tariff_type
self.consumer_price_index = consumer_price_index
self.interest_rate = interest_rate
self.discount_rate = discount_rate
self.credit_years = credit_years
self.percentage_credit = percentage_credit
self.costs = self.costs_archetype()
self.feasibility = True
self.fitness_score = 0
self.individual = {}
def system_components(self):
"""
Extracts system components (generation and storage) for a given energy system.
:return: Dictionary of system components.
"""
self.individual['Generation Components'] = []
self.individual['Energy Storage Components'] = []
self.individual['End of Life Cost'] = self.costs.end_of_life_cost
for generation_component in self.energy_system.generation_systems:
investment_cost, reposition_cost, lifetime = self.unit_investment_cost('Generation',
generation_component.system_type)
maintenance_cost = self.unit_maintenance_cost(generation_component)
if generation_component.system_type == cte.PHOTOVOLTAIC:
heating_capacity = None
cooling_capacity = None
heat_efficiency = None
cooling_efficiency = None
unit_fuel_cost = 0
else:
heating_capacity = 0
cooling_capacity = 0
heat_efficiency = generation_component.heat_efficiency
cooling_efficiency = generation_component.cooling_efficiency
unit_fuel_cost = self.fuel_cost_per_kwh(generation_component.fuel_type, 'fixed')
self.individual['Generation Components'].append({
'type': generation_component.system_type,
'heating_capacity': heating_capacity,
'cooling_capacity': cooling_capacity,
'electricity_capacity': 0,
'nominal_heating_efficiency': heat_efficiency,
'nominal_cooling_efficiency': cooling_efficiency,
'nominal_electricity_efficiency': generation_component.electricity_efficiency,
'fuel_type': generation_component.fuel_type,
'unit_investment_cost': investment_cost,
'unit_reposition_cost': reposition_cost,
'unit_fuel_cost(CAD/kWh)': unit_fuel_cost,
'unit_maintenance_cost': maintenance_cost,
'lifetime': lifetime
})
if generation_component.energy_storage_systems is not None:
for energy_storage_system in generation_component.energy_storage_systems:
investment_cost, reposition_cost, lifetime = (
self.unit_investment_cost('Storage',
f'{energy_storage_system.type_energy_stored}_storage'))
if energy_storage_system.type_energy_stored == cte.THERMAL:
heating_coil_capacity = energy_storage_system.heating_coil_capacity
heating_coil_fuel_cost = self.fuel_cost_per_kwh(f'{cte.ELECTRICITY}', 'fixed')
volume = 0
capacity = None
else:
heating_coil_capacity = None
heating_coil_fuel_cost = None
volume = None
capacity = 0
self.individual['Energy Storage Components'].append(
{'type': f'{energy_storage_system.type_energy_stored}_storage',
'capacity': capacity,
'volume': volume,
'heating_coil_capacity': heating_coil_capacity,
'unit_investment_cost': investment_cost,
'unit_reposition_cost': reposition_cost,
'heating_coil_fuel_cost': heating_coil_fuel_cost,
'unit_maintenance_cost': 0,
'lifetime': lifetime
})
def initialization(self):
"""
Assigns initial sizes to generation and storage components based on heating and cooling design loads and
available space in the building.
:return:
"""
self.system_components()
generation_components = self.individual['Generation Components']
storage_components = self.individual['Energy Storage Components']
for generation_component in generation_components:
if generation_component[
'nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
if self.heating_design_load is not None:
generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
else:
if cte.HEATING in self.demand_types:
generation_component['heating_capacity'] = random.uniform(0, max(
self.design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
generation_component['heating_capacity'] = random.uniform(0, max(
self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
generation_component['heating_capacity'] = None
if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in self.demand_types:
if self.cooling_design_load is not None:
generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
else:
generation_component['cooling_capacity'] = random.uniform(0, max(
self.design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
generation_component['cooling_capacity'] = None
if generation_component['nominal_electricity_efficiency'] is None:
generation_component['electricity_capacity'] = None
for storage_component in storage_components:
if storage_component['type'] == f'{cte.THERMAL}_storage':
storage_component['volume'] = random.uniform(0, 0.01 * self.available_space)
if storage_component['heating_coil_capacity'] is not None:
if self.heating_design_load is not None:
storage_component['heating_coil_capacity'] = random.uniform(0, self.heating_design_load)
else:
if cte.HEATING in self.demand_types:
storage_component['heating_coil_capacity'] = random.uniform(0, max(
self.design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
storage_component['heating_coil_capacity'] = random.uniform(0, max(
self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
def score_evaluation(self):
self.system_simulation()
self.individual['feasible'] = self.feasibility
lcc = 0
total_energy_consumption = 0
if self.feasibility:
if 'cost' in self.optimization_scenario:
investment_cost = 0
operation_cost_year_0 = 0
maintenance_cost_year_0 = 0
for generation_system in self.individual['Generation Components']:
heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
'heating_capacity']
cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
'cooling_capacity']
capacity = max(heating_capacity, cooling_capacity)
investment_cost += capacity * generation_system['unit_investment_cost'] / 1000
maintenance_cost_year_0 += capacity * generation_system['unit_maintenance_cost'] / 1000
operation_cost_year_0 += (generation_system['total_energy_consumption(kWh)'] *
generation_system['unit_fuel_cost(CAD/kWh)'])
for storage_system in self.individual['Energy Storage Components']:
if cte.THERMAL in storage_system['type']:
investment_cost += storage_system['volume'] * storage_system['unit_investment_cost']
if storage_system['heating_coil_capacity'] is not None:
operation_cost_year_0 += (storage_system['total_energy_consumption(kWh)'] *
storage_system['heating_coil_fuel_cost'])
lcc = self.life_cycle_cost_calculation(investment_cost=investment_cost,
operation_cost_year_0=operation_cost_year_0,
maintenance_cost_year_0=maintenance_cost_year_0)
self.individual['lcc'] = lcc
if 'energy_consumption' in self.optimization_scenario:
total_energy_consumption = 0
for generation_system in self.individual['Generation Components']:
total_energy_consumption += generation_system['total_energy_consumption(kWh)']
for storage_system in self.individual['Energy Storage Components']:
if cte.THERMAL in storage_system['type'] and storage_system['heating_coil_capacity'] is not None:
total_energy_consumption += storage_system['total_energy_consumption(kWh)']
self.individual['total_energy_consumption'] = total_energy_consumption
# Fitness score based on the optimization scenario
if self.optimization_scenario == 'cost':
self.fitness_score = lcc
self.individual['fitness_score'] = lcc
elif self.optimization_scenario == 'energy_consumption':
self.fitness_score = total_energy_consumption
self.individual['fitness_score'] = total_energy_consumption
elif self.optimization_scenario == 'cost_energy_consumption' or self.optimization_scenario == 'energy_consumption_cost':
self.fitness_score = (lcc, total_energy_consumption)
self.individual['fitness_score'] = (lcc, total_energy_consumption)
else:
lcc = float('inf')
total_energy_consumption = float('inf')
self.individual['lcc'] = lcc
self.individual['total_energy_consumption'] = total_energy_consumption
if self.optimization_scenario == 'cost_energy_consumption' or self.optimization_scenario == 'energy_consumption_cost':
self.individual['fitness_score'] = (float('inf'), float('inf'))
self.fitness_score = (float('inf'), float('inf'))
else:
self.individual['fitness_score'] = float('inf')
self.fitness_score = float('inf')
def system_simulation(self):
"""
The method to run the energy system model using the existing models in the energy_system_modelling_package.
Based on cluster id and demands, model is imported and run.
:return: dictionary of results
"""
if self.building.energy_systems_archetype_cluster_id == '1':
if cte.HEATING in self.demand_types:
boiler = self.energy_system.generation_systems[0]
boiler.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
hp = self.energy_system.generation_systems[1]
hp.nominal_heat_output = self.individual['Generation Components'][1]['heating_capacity']
tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
tes.volume = self.individual['Energy Storage Components'][0]['volume']
tes.height = self.building.average_storey_height - 1
tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
heating_demand_joules = self.design_period_energy_demands[cte.HEATING]['demands']
heating_peak_load_watts = max(self.design_period_energy_demands[cte.HEATING]) if \
(self.heating_design_load is not None) else self.building.heating_peak_load[cte.YEAR][0]
upper_limit_tes_heating = 55
design_period_start_index = self.design_period_energy_demands[cte.HEATING]['start_index']
design_period_end_index = self.design_period_energy_demands[cte.HEATING]['end_index']
outdoor_temperature = self.building.external_temperature[cte.HOUR][
design_period_start_index:design_period_end_index]
results = HeatPumpBoilerTesHeating(hp=hp,
boiler=boiler,
tes=tes,
hourly_heating_demand_joules=heating_demand_joules,
heating_peak_load_watts=heating_peak_load_watts,
upper_limit_tes=upper_limit_tes_heating,
outdoor_temperature=outdoor_temperature,
dt=self.dt).simulation()
if min(results['TES Temperature']) < 35:
self.feasibility = False
elif cte.DOMESTIC_HOT_WATER in self.demand_types:
hp = self.energy_system.generation_systems[0]
hp.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
tes.volume = self.individual['Energy Storage Components'][0]['volume']
tes.height = self.building.average_storey_height - 1
tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
dhw_demand_joules = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']
upper_limit_tes = 65
design_period_start_index = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['start_index']
design_period_end_index = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['end_index']
outdoor_temperature = self.building.external_temperature[cte.HOUR][
design_period_start_index:design_period_end_index]
results = DomesticHotWaterHeatPumpTes(hp=hp,
tes=tes,
hourly_dhw_demand_joules=dhw_demand_joules,
upper_limit_tes=upper_limit_tes,
outdoor_temperature=outdoor_temperature,
dt=self.dt).simulation()
if min(results['DHW TES Temperature']) < 55:
self.feasibility = False
if self.feasibility:
generation_system_types = [generation_system.system_type for generation_system in
self.energy_system.generation_systems]
for generation_component in self.individual['Generation Components']:
if generation_component['type'] in generation_system_types:
index = generation_system_types.index(generation_component['type'])
for demand_type in self.demand_types:
if demand_type in self.energy_system.generation_systems[index].energy_consumption:
generation_component['total_energy_consumption(kWh)'] = (sum(
self.energy_system.generation_systems[index].energy_consumption[demand_type][cte.HOUR]) / 3.6e6)
for storage_component in self.individual['Energy Storage Components']:
if storage_component['type'] == f'{cte.THERMAL}_storage' and storage_component[
'heating_coil_capacity'] is not None:
for generation_system in self.energy_system.generation_systems:
if generation_system.energy_storage_systems is not None:
for storage_system in generation_system.energy_storage_systems:
if storage_system.type_energy_stored == cte.THERMAL:
for demand_type in self.demand_types:
if demand_type in storage_system.heating_coil_energy_consumption:
storage_component['total_energy_consumption(kWh)'] = (sum(
storage_system.heating_coil_energy_consumption[demand_type][cte.HOUR]) / 3.6e6)
def life_cycle_cost_calculation(self, investment_cost, operation_cost_year_0, maintenance_cost_year_0,
life_cycle_duration=41):
"""
Calculating the life cycle cost of the energy system. The original cost workflow in hub is not used to reduce
computation time.Here are the steps:
1- Costs catalog and different components are imported.
2- Capital costs (investment and reposition) are calculated and appended to a list to have the capital cash
flow.
3-
:param maintenance_cost_year_0:
:param operation_cost_year_0:
:param investment_cost:
:param life_cycle_duration:
:return:
"""
capital_costs_cash_flow = [investment_cost]
operational_costs_cash_flow = [0]
maintenance_costs_cash_flow = [0]
end_of_life_costs = [0] * (life_cycle_duration + 1)
for i in range(1, life_cycle_duration + 1):
yearly_operational_cost = math.pow(1 + self.fuel_price_index, i) * operation_cost_year_0
yearly_maintenance_cost = math.pow(1 + self.consumer_price_index, i) * maintenance_cost_year_0
yearly_capital_cost = 0
for generation_system in self.individual['Generation Components']:
if (i % generation_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
cost_increase = math.pow(1 + self.consumer_price_index, i)
heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
'heating_capacity']
cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
'cooling_capacity']
capacity = max(heating_capacity, cooling_capacity)
yearly_capital_cost += generation_system['unit_reposition_cost'] * capacity * cost_increase / 1000
yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
investment_cost * self.percentage_credit)
for storage_system in self.individual['Energy Storage Components']:
if (i % storage_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
cost_increase = math.pow(1 + self.consumer_price_index, i)
capacity = storage_system['volume'] if cte.THERMAL in storage_system['type'] else storage_system['capacity']
yearly_capital_cost += storage_system['unit_reposition_cost'] * capacity * cost_increase
yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
investment_cost * self.percentage_credit)
capital_costs_cash_flow.append(yearly_capital_cost)
operational_costs_cash_flow.append(yearly_operational_cost)
maintenance_costs_cash_flow.append(yearly_maintenance_cost)
for year in range(1, life_cycle_duration + 1):
price_increase = math.pow(1 + self.consumer_price_index, year)
if year == life_cycle_duration:
end_of_life_costs[year] = (
self.building.thermal_zones_from_internal_zones[0].total_floor_area *
self.individual['End of Life Cost'] * price_increase
)
life_cycle_capital_cost = npf.npv(self.discount_rate, capital_costs_cash_flow)
life_cycle_operational_cost = npf.npv(self.discount_rate, operational_costs_cash_flow)
life_cycle_maintenance_cost = npf.npv(self.discount_rate, maintenance_costs_cash_flow)
life_cycle_end_of_life_cost = npf.npv(self.discount_rate, end_of_life_costs)
total_life_cycle_cost = life_cycle_capital_cost + life_cycle_operational_cost + life_cycle_maintenance_cost + life_cycle_end_of_life_cost
return total_life_cycle_cost
def costs_archetype(self):
costs_catalogue = CostsCatalogFactory('montreal_new').catalog.entries().archetypes
dictionary = Dictionaries().hub_function_to_montreal_custom_costs_function
costs_archetype = None
for archetype in costs_catalogue:
if dictionary[str(self.building.function)] == str(archetype.function):
costs_archetype = archetype
return costs_archetype
def unit_investment_cost(self, component_category, component_type):
"""
Reading the investment and reposition costs of any component from costs catalogue
:param component_category: Due to the categorizations in the cost catalogue, we need this parameter to realize if
the component is a generation or storage component
:param component_type: Type of the component
:return:
"""
investment_cost = 0
reposition_cost = 0
lifetime = 0
name = ''
capital_costs_chapter = self.costs.capital_cost.chapter('D_services')
if component_category == 'Generation':
generation_systems = self.energy_system.generation_systems
for generation_system in generation_systems:
if component_type == generation_system.system_type:
if generation_system.system_type == cte.HEAT_PUMP:
name += (
generation_system.source_medium.lower() + '_to_' + generation_system.supply_medium.lower() + '_' +
generation_system.system_type.lower().replace(' ', '_'))
elif generation_system.system_type == cte.BOILER:
if generation_system.fuel_type == cte.ELECTRICITY:
name += cte.ELECTRICAL.lower() + f'_{generation_system.system_type}'.lower()
else:
name += generation_system.fuel_type.lower() + f'_{generation_system.system_type}'.lower()
elif generation_system.system_type == cte.PHOTOVOLTAIC:
name += 'D2010_photovoltaic_system'
else:
if cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
name += 'template_heat'
else:
name += 'template_cooling'
for item in capital_costs_chapter.items:
if name in item.type:
investment_cost += float(capital_costs_chapter.item(item.type).initial_investment[0])
reposition_cost += float(capital_costs_chapter.item(item.type).reposition[0])
lifetime += float(capital_costs_chapter.item(item.type).lifetime)
elif component_category == 'Storage':
for generation_system in self.energy_system.generation_systems:
if generation_system.energy_storage_systems is not None:
for energy_storage_system in generation_system.energy_storage_systems:
if energy_storage_system.type_energy_stored == cte.THERMAL:
if energy_storage_system.heating_coil_capacity is not None:
investment_cost += float(capital_costs_chapter.item('D306010_storage_tank').initial_investment[0])
reposition_cost += float(capital_costs_chapter.item('D306010_storage_tank').reposition[0])
lifetime += float(capital_costs_chapter.item('D306010_storage_tank').lifetime)
else:
investment_cost += float(
capital_costs_chapter.item('D306020_storage_tank_with_coil').initial_investment[0])
reposition_cost += float(capital_costs_chapter.item('D306020_storage_tank_with_coil').reposition[0])
lifetime += float(capital_costs_chapter.item('D306020_storage_tank_with_coil').lifetime)
return investment_cost, reposition_cost, lifetime
def unit_maintenance_cost(self, generation_system):
hvac_maintenance = self.costs.operational_cost.maintenance_hvac
pv_maintenance = self.costs.operational_cost.maintenance_pv
maintenance_cost = 0
component = None
if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
component = 'air_source_heat_pump'
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
component = 'ground_source_heat_pump'
elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
component = 'water_source_heat_pump'
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
component = 'gas_boiler'
elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
component = 'electric_boiler'
elif generation_system.system_type == cte.PHOTOVOLTAIC:
maintenance_cost += pv_maintenance
else:
if cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
component = 'general_heating_equipment'
else:
component = 'general_cooling_equipment'
for item in hvac_maintenance:
if item.type == component:
maintenance_cost += item.maintenance[0]
return maintenance_cost
def fuel_cost_per_kwh(self, fuel_type, fuel_cost_tariff_type):
fuel_cost = 0
catalog_fuels = self.costs.operational_cost.fuels
for fuel in catalog_fuels:
if fuel_type == fuel.type and fuel_cost_tariff_type == fuel.variable.rate_type:
if fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'fixed':
fuel_cost = fuel.variable.values[0]
elif fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'variable':
fuel_cost = fuel.variable.values[0]
else:
if fuel.type == cte.BIOMASS:
conversion_factor = 1
else:
conversion_factor = fuel.density[0]
fuel_cost = fuel.variable.values[0] / (conversion_factor * fuel.lower_heating_value[0] * 0.277)
return fuel_cost

418
energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm.py
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import copy
import math
import random
import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
Individual
import matplotlib.pyplot as plt
class MultiObjectiveGeneticAlgorithm:
def __init__(self, population_size=100, generations=50, crossover_rate=0.8, mutation_rate=0.1,
optimization_scenario=None, output_path=None):
self.population_size = population_size
self.population = []
self.generations = generations
self.crossover_rate = crossover_rate
self.mutation_rate = mutation_rate
self.optimization_scenario = optimization_scenario
self.list_of_solutions = []
self.best_solution = None
self.best_solution_generation = None
self.output_path = output_path
# Initialize Population
def initialize_population(self, building, energy_system):
design_period_energy_demands = self.design_period_identification(building)
attempts = 0
max_attempts = self.population_size * 5
while len(self.population) < self.population_size and attempts < max_attempts:
individual = Individual(building=building,
energy_system=energy_system,
design_period_energy_demands=design_period_energy_demands,
optimization_scenario=self.optimization_scenario)
individual.initialization()
attempts += 1
if self.initial_population_feasibility_check(individual, energy_system.demand_types,
design_period_energy_demands):
self.population.append(individual)
if len(self.population) < self.population_size:
raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
f"Only {len(self.population)} feasible individuals were generated.")
@staticmethod
def initial_population_feasibility_check(individual, demand_types, design_period_demands):
total_heating_capacity = sum(
component['heating_capacity'] for component in individual.individual['Generation Components']
if component['heating_capacity'] is not None
)
total_cooling_capacity = sum(
component['cooling_capacity'] for component in individual.individual['Generation Components']
if component['cooling_capacity'] is not None
)
max_heating_demand = max(design_period_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
max_cooling_demand = max(design_period_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES
max_dhw_demand = max(design_period_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
if cte.HEATING in demand_types and total_heating_capacity < 0.5 * max_heating_demand:
return False
if cte.DOMESTIC_HOT_WATER in demand_types and total_heating_capacity < 0.5 * max_dhw_demand:
return False
if cte.COOLING in demand_types and total_cooling_capacity < 0.5 * max_cooling_demand:
return False
total_volume = sum(
component['volume'] for component in individual.individual['Energy Storage Components']
if component['volume'] is not None
)
max_storage_volume = individual.available_space * 0.1
if total_volume > max_storage_volume:
return False
return True
def nsga2_selection(self, population, fronts, crowding_distances):
new_population = []
i = 0
while len(new_population) + len(fronts[i]) <= self.population_size:
for index in fronts[i]:
# Skip individuals with infinite fitness values to avoid propagation
if not math.isinf(self.population[index].individual['fitness_score'][0]) and \
not math.isinf(self.population[index].individual['fitness_score'][1]):
new_population.append(population[index])
i += 1
if i >= len(fronts):
break
if len(new_population) < self.population_size and i < len(fronts):
sorted_front = sorted(fronts[i], key=lambda x: crowding_distances[i][x], reverse=True)
for index in sorted_front:
if len(new_population) < self.population_size:
if not math.isinf(self.population[index].individual['fitness_score'][0]) and \
not math.isinf(self.population[index].individual['fitness_score'][1]):
new_population.append(population[index])
else:
break
return new_population
def fast_non_dominated_sort(self):
population_size = self.population_size
s = [[] for _ in range(population_size)]
front = [[]]
n = [0] * population_size
rank = [0] * population_size
for p in range(population_size):
s[p] = []
n[p] = 0
for q in range(population_size):
if self.dominates(self.population[p], self.population[q]):
s[p].append(q)
elif self.dominates(self.population[q], self.population[p]):
n[p] += 1
if n[p] == 0:
rank[p] = 0
front[0].append(p)
i = 0
while front[i]:
next_front = set()
for p in front[i]:
for q in s[p]:
n[q] -= 1
if n[q] == 0:
rank[q] = i + 1
next_front.add(q)
i += 1
front.append(list(next_front))
del front[-1]
return front
@staticmethod
def dominates(individual1, individual2):
lcc1, lce1 = individual1.individual['fitness_score']
lcc2, lce2 = individual2.individual['fitness_score']
return (lcc1 <= lcc2 and lce1 <= lce2) and (lcc1 < lcc2 or lce1 < lce2)
def calculate_crowding_distance(self, front):
crowding_distance = [0] * len(self.population)
for objective in ['lcc', 'total_energy_consumption']:
sorted_front = sorted(front, key=lambda x: self.population[x].individual[objective])
# Set distances to finite large numbers rather than `inf`
crowding_distance[sorted_front[0]] = float(1e9)
crowding_distance[sorted_front[-1]] = float(1e9)
objective_min = self.population[sorted_front[0]].individual[objective]
objective_max = self.population[sorted_front[-1]].individual[objective]
if objective_max != objective_min:
for i in range(1, len(sorted_front) - 1):
crowding_distance[sorted_front[i]] += (
(self.population[sorted_front[i + 1]].individual[objective] -
self.population[sorted_front[i - 1]].individual[objective]) /
(objective_max - objective_min))
return crowding_distance
def crossover(self, parent1, parent2):
"""
Crossover between two parents to produce two children.
swaps generation components and storage components between the two parents with a 50% chance.
:param parent1: First parent individual.
:param parent2: second parent individual.
:return: Two child individuals (child1 and child2).
"""
if random.random() < self.crossover_rate:
# Deep copy of the parents to create children
child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
# Crossover for Generation Components
for i in range(len(parent1.individual['Generation Components'])):
if random.random() < 0.5:
# swap the entire generation component
child1.individual['Generation Components'][i], child2.individual['Generation Components'][i] = (
child2.individual['Generation Components'][i],
child1.individual['Generation Components'][i]
)
# Crossover for Energy storage Components
for i in range(len(parent1.individual['Energy Storage Components'])):
if random.random() < 0.5:
# swap the entire storage component
child1.individual['Energy Storage Components'][i], child2.individual['Energy Storage Components'][i] = (
child2.individual['Energy Storage Components'][i],
child1.individual['Energy Storage Components'][i]
)
return child1, child2
else:
# If crossover does not happen, return copies of the original parents
return copy.deepcopy(parent1), copy.deepcopy(parent2)
def mutate(self, individual, building, energy_system):
"""
Mutates the individual's generation and storage components.
- `individual`: The individual to mutate (contains generation and storage components).
- `building`: Building data that contains constraints such as peak heating load and available space.
Returns the mutated individual.
"""
design_period_energy_demands = self.design_period_identification(building)
# Mutate Generation Components
for generation_component in individual['Generation Components']:
if random.random() < self.mutation_rate:
if (generation_component['nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in
energy_system.demand_types):
# Mutate heating capacity
if cte.HEATING in energy_system.demand_types:
generation_component['heating_capacity'] = random.uniform(
0, max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
generation_component['heating_capacity'] = random.uniform(
0, max(design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in energy_system.demand_types:
# Mutate cooling capacity
generation_component['cooling_capacity'] = random.uniform(
0, max(design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
# Mutate storage Components
for storage_component in individual['Energy Storage Components']:
if random.random() < self.mutation_rate:
if storage_component['type'] == f'{cte.THERMAL}_storage':
# Mutate the volume of thermal storage
max_available_space = 0.01 * building.volume / building.storeys_above_ground
storage_component['volume'] = random.uniform(0, max_available_space)
if storage_component['heating_coil_capacity'] is not None:
if cte.HEATING in energy_system.demand_types:
storage_component['heating_coil_capacity'] = random.uniform(0, max(
design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
storage_component['heating_coil_capacity'] = random.uniform(0, max(
design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
return individual
def solve_ga(self, building, energy_system):
self.initialize_population(building, energy_system)
for individual in self.population:
individual.score_evaluation()
pareto_population = []
for generation in range(1, self.generations + 1):
print(f"Generation {generation}")
fronts = self.fast_non_dominated_sort()
# Ensure the front calculation is valid
if not fronts or not fronts[0]:
print("Warning: No valid non-dominated front found.")
continue
# Calculate crowding distances and select individuals
crowding_distances = [self.calculate_crowding_distance(front) for front in fronts]
self.population = self.nsga2_selection(self.population, fronts, crowding_distances)
# Add only valid indices to pareto_population
pareto_population.extend([self.population[i] for i in fronts[0] if i < len(self.population)])
# Check population bounds
if len(pareto_population) > self.population_size:
pareto_population = pareto_population[:self.population_size]
# Generate the next population through crossover and mutation
next_population = []
while len(next_population) < self.population_size:
parent1, parent2 = random.choice(self.population), random.choice(self.population)
child1, child2 = self.crossover(parent1, parent2)
self.mutate(child1.individual, building, energy_system)
self.mutate(child2.individual, building, energy_system)
child1.score_evaluation()
child2.score_evaluation()
next_population.extend([child1, child2])
self.population = next_population[:self.population_size]
# Ensure pareto_population contains the correct non-dominated individuals before TOPSIS
if not pareto_population:
print("No Pareto solutions found during optimization.")
return None
# Recalculate pareto front indices based on updated pareto_population
fronts = self.fast_non_dominated_sort()
pareto_front_indices = fronts[0] if fronts else []
pareto_front_indices = [i for i in pareto_front_indices if i < len(pareto_population)]
print(f"Final pareto_front_indices: {pareto_front_indices}, pareto_population size: {len(pareto_population)}")
if not pareto_front_indices:
print("No valid solution found after TOPSIS due to empty pareto front indices.")
return None
global_pareto_front = [pareto_population[i] for i in pareto_front_indices]
# Get the top N solutions with TOPSIS
top_n = 3 # Adjust this value based on how many top solutions you want to explore
self.best_solution = self.topsis_decision_making(global_pareto_front, top_n=top_n)
# Print the top N solutions
if self.best_solution:
print("Top solutions after TOPSIS:")
for idx, solution in enumerate(self.best_solution, 1):
print(f"Solution {idx}: LCC = {solution.individual['lcc']}, "
f"LCE = {solution.individual['total_energy_consumption']}")
else:
print("No valid solutions found after TOPSIS.")
if pareto_population:
self.plot_pareto_front(pareto_population)
return self.best_solution
@staticmethod
def design_period_identification(building):
def get_start_end_indices(max_day_index, total_days):
if max_day_index > 0 and max_day_index < total_days - 1:
start_index = (max_day_index - 1) * 24
end_index = (max_day_index + 2) * 24
elif max_day_index == 0:
start_index = 0
end_index = (max_day_index + 2) * 24
else:
start_index = (max_day_index - 1) * 24
end_index = total_days * 24
return start_index, end_index
# Calculate daily demands
heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.heating_demand[cte.HOUR]), 24)]
cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.cooling_demand[cte.HOUR]), 24)]
dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
# Get the day with maximum demand for each type
heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
# Get the start and end indices for each demand type
heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
# Return the design period energy demands
return {
f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
'start_index': heating_start, 'end_index': heating_end},
f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
'start_index': cooling_start, 'end_index': cooling_end},
f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
'start_index': dhw_start, 'end_index': dhw_end}
}
def topsis_decision_making(self, pareto_front, top_n=5):
"""
Perform TOPSIS decision-making to choose the best solutions from the Pareto front.
:param pareto_front: List of individuals in the Pareto front
:param top_n: Number of top solutions to select based on TOPSIS ranking
:return: List of top N individuals based on TOPSIS ranking
"""
# Filter out infinite values from the pareto front before processing
pareto_front = [ind for ind in pareto_front if all(math.isfinite(v) for v in ind.individual['fitness_score'])]
if not pareto_front:
return None
# Step 1: Normalize the objective functions (cost and energy consumption)
min_lcc = min([ind.individual['lcc'] for ind in pareto_front])
max_lcc = max([ind.individual['lcc'] for ind in pareto_front])
min_lce = min([ind.individual['total_energy_consumption'] for ind in pareto_front])
max_lce = max([ind.individual['total_energy_consumption'] for ind in pareto_front])
normalized_pareto_front = []
for ind in pareto_front:
normalized_lcc = (ind.individual['lcc'] - min_lcc) / (max_lcc - min_lcc) if max_lcc > min_lcc else 0
normalized_lce = (ind.individual['total_energy_consumption'] - min_lce) / (
max_lce - min_lce) if max_lce > min_lce else 0
normalized_pareto_front.append((ind, normalized_lcc, normalized_lce))
# Step 2: Calculate the ideal and worst solutions
ideal_solution = (0, 0) # Ideal is minimum LCC and minimum LCE (0, 0 after normalization)
worst_solution = (1, 1) # Worst is maximum LCC and maximum LCE (1, 1 after normalization)
# Step 3: Calculate the distance to the ideal and worst solutions
best_distances = []
worst_distances = []
for ind, normalized_lcc, normalized_lce in normalized_pareto_front:
distance_to_ideal = math.sqrt(
(normalized_lcc - ideal_solution[0]) ** 2 + (normalized_lce - ideal_solution[1]) ** 2)
distance_to_worst = math.sqrt(
(normalized_lcc - worst_solution[0]) ** 2 + (normalized_lce - worst_solution[1]) ** 2)
best_distances.append(distance_to_ideal)
worst_distances.append(distance_to_worst)
# Step 4: Calculate relative closeness to the ideal solution
similarity = [worst / (best + worst) for best, worst in zip(best_distances, worst_distances)]
# Step 5: Select the top N individuals with the highest similarity scores
top_indices = sorted(range(len(similarity)), key=lambda i: similarity[i], reverse=True)[:top_n]
top_solutions = [pareto_front[i] for i in top_indices]
# Plot the similarity scores
self.plot_topsis_similarity(similarity)
return top_solutions
@staticmethod
def plot_topsis_similarity(similarity):
"""
Plot the TOPSIS similarity scores for visualization.
:param similarity: List of similarity scores for each individual in the Pareto front
"""
plt.figure(figsize=(10, 6))
plt.plot(range(len(similarity)), similarity, 'bo-', label='TOPSIS Similarity Scores')
plt.xlabel('Pareto Front Solution Index')
plt.ylabel('Similarity Score')
plt.title('TOPSIS Similarity Scores for Pareto Front Solutions')
plt.legend()
plt.grid(True)
plt.show()
@staticmethod
def plot_pareto_front(pareto_population):
# Extract LCC and LCE for plotting
lcc_values = [individual.individual['lcc'] for individual in pareto_population]
lce_values = [individual.individual['total_energy_consumption'] for individual in pareto_population]
plt.figure(figsize=(10, 6))
plt.scatter(lcc_values, lce_values, color='blue', label='Pareto Front', alpha=0.6, edgecolors='w', s=80)
plt.title('Pareto Front for Life Cycle Cost vs Life Cycle Energy')
plt.xlabel('Life Cycle Cost (LCC)')
plt.ylabel('Life Cycle Energy (LCE)')
plt.grid(True)
plt.legend()
plt.show()

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energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm_rethinking.py
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import copy
import math
import random
import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
Individual
import matplotlib.pyplot as plt
import time
class MultiObjectiveGeneticAlgorithm:
def __init__(self, population_size=100, generations=50, crossover_rate=0.8, mutation_rate=0.1,
optimization_scenario=None, output_path=None):
self.population_size = population_size
self.population = []
self.generations = generations
self.crossover_rate = crossover_rate
self.mutation_rate = mutation_rate
self.optimization_scenario = optimization_scenario
self.list_of_solutions = []
self.best_solution = None
self.best_solution_generation = None
self.output_path = output_path
# Initialize Population
def initialize_population(self, building, energy_system):
design_period_start_time = time.time()
design_period_energy_demands = self.design_period_identification(building)
design_period_time = time.time() - design_period_start_time
print(f"design period identification took {design_period_time:.2f} seconds")
initializing_time_start = time.time()
attempts = 0
max_attempts = self.population_size * 20
while len(self.population) < self.population_size and attempts < max_attempts:
individual = Individual(building=building,
energy_system=energy_system,
design_period_energy_demands=design_period_energy_demands,
optimization_scenario=self.optimization_scenario)
individual.initialization()
individual.score_evaluation()
attempts += 1
if individual.feasibility:
self.population.append(individual)
if len(self.population) < self.population_size:
raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
f"Only {len(self.population)} feasible individuals were generated.")
initializing_time = time.time() - initializing_time_start
print(f"initializing took {initializing_time:.2f} seconds")
@staticmethod
def design_period_identification(building):
def get_start_end_indices(max_day_index, total_days):
if max_day_index > 0 and max_day_index < total_days - 1:
start_index = (max_day_index - 1) * 24
end_index = (max_day_index + 2) * 24
elif max_day_index == 0:
start_index = 0
end_index = (max_day_index + 2) * 24
else:
start_index = (max_day_index - 1) * 24
end_index = total_days * 24
return start_index, end_index
# Calculate daily demands
heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.heating_demand[cte.HOUR]), 24)]
cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.cooling_demand[cte.HOUR]), 24)]
dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
# Get the day with maximum demand for each type
heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
# Get the start and end indices for each demand type
heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
# Return the design period energy demands
return {
f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
'start_index': heating_start, 'end_index': heating_end},
f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
'start_index': cooling_start, 'end_index': cooling_end},
f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
'start_index': dhw_start, 'end_index': dhw_end}
}
def solve_ga(self, building, energy_system):
self.initialize_population(building, energy_system)
for individual in self.population:
print(individual.individual)
print([ind.fitness_score for ind in self.population])

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energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/single_objective_genetic_algorithm.py
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import copy
import math
import random
import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
Individual
class SingleObjectiveGeneticAlgorithm:
def __init__(self, population_size=100, generations=20, crossover_rate=0.8, mutation_rate=0.1,
optimization_scenario=None, output_path=None):
self.population_size = population_size
self.population = []
self.generations = generations
self.crossover_rate = crossover_rate
self.mutation_rate = mutation_rate
self.optimization_scenario = optimization_scenario
self.list_of_solutions = []
self.best_solution = None
self.best_solution_generation = None
self.output_path = output_path
# Initialize Population
def initialize_population(self, building, energy_system):
"""
Initialize a population of individuals with feasible configurations for optimizing the sizes of
generation and storage components of an energy system.
:param building: Building object with associated data
:param energy_system: Energy system to optimize
"""
design_period_energy_demands = self.design_period_identification(building)
attempts = 0 # Track attempts to avoid an infinite loop in rare cases
max_attempts = self.population_size * 5
while len(self.population) < self.population_size and attempts < max_attempts:
individual = Individual(building=building,
energy_system=energy_system,
design_period_energy_demands=design_period_energy_demands,
optimization_scenario=self.optimization_scenario)
individual.initialization()
attempts += 1
# Enhanced feasibility check
if self.initial_population_feasibility_check(individual, energy_system.demand_types, design_period_energy_demands):
self.population.append(individual)
# Raise an error or print a warning if the population size goal is not met after max_attempts
if len(self.population) < self.population_size:
raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
f"Only {len(self.population)} feasible individuals were generated.")
@staticmethod
def initial_population_feasibility_check(individual, demand_types, design_period_demands):
"""
Check if the individual meets basic feasibility requirements for heating, cooling, and DHW capacities
and storage volume.
:param individual: Individual to check
:param demand_types: List of demand types (e.g., heating, cooling, DHW)
:param design_period_demands: Design period demand values for heating, cooling, and DHW
:return: True if feasible, False otherwise
"""
# Calculate total heating and cooling capacities
total_heating_capacity = sum(
component['heating_capacity'] for component in individual.individual['Generation Components']
if component['heating_capacity'] is not None
)
total_cooling_capacity = sum(
component['cooling_capacity'] for component in individual.individual['Generation Components']
if component['cooling_capacity'] is not None
)
# Maximum demands for each demand type (converted to kW)
max_heating_demand = max(design_period_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
max_cooling_demand = max(design_period_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES
max_dhw_demand = max(design_period_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
# Check heating capacity feasibility
if cte.HEATING in demand_types and total_heating_capacity < 0.5 * max_heating_demand:
return False
# Check DHW capacity feasibility
if cte.DOMESTIC_HOT_WATER in demand_types and total_heating_capacity < 0.5 * max_dhw_demand:
return False
# Check cooling capacity feasibility
if cte.COOLING in demand_types and total_cooling_capacity < 0.5 * max_cooling_demand:
return False
# Check storage volume feasibility
total_volume = sum(
component['volume'] for component in individual.individual['Energy Storage Components']
if component['volume'] is not None
)
# Limit storage to 10% of building's available space
max_storage_volume = individual.available_space * 0.1
if total_volume > max_storage_volume:
return False
return True # Feasible if all checks are passed
def order_population(self):
"""
ordering the population based on the fitness score in ascending order
:return:
"""
self.population = sorted(self.population, key=lambda x: x.fitness_score)
def tournament_selection(self):
selected = []
for _ in range(len(self.population)):
i, j = random.sample(range(self.population_size), 2)
if self.population[i].individual['fitness_score'] < self.population[j].individual['fitness_score']:
selected.append(copy.deepcopy(self.population[i]))
else:
selected.append(copy.deepcopy(self.population[j]))
return selected
def crossover(self, parent1, parent2):
"""
Crossover between two parents to produce two children.
swaps generation components and storage components between the two parents with a 50% chance.
:param parent1: First parent individual.
:param parent2: second parent individual.
:return: Two child individuals (child1 and child2).
"""
if random.random() < self.crossover_rate:
# Deep copy of the parents to create children
child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
# Crossover for Generation Components
for i in range(len(parent1.individual['Generation Components'])):
if random.random() < 0.5:
# swap the entire generation component
child1.individual['Generation Components'][i], child2.individual['Generation Components'][i] = (
child2.individual['Generation Components'][i],
child1.individual['Generation Components'][i]
)
# Crossover for Energy storage Components
for i in range(len(parent1.individual['Energy Storage Components'])):
if random.random() < 0.5:
# swap the entire storage component
child1.individual['Energy Storage Components'][i], child2.individual['Energy Storage Components'][i] = (
child2.individual['Energy Storage Components'][i],
child1.individual['Energy Storage Components'][i]
)
return child1, child2
else:
# If crossover does not happen, return copies of the original parents
return copy.deepcopy(parent1), copy.deepcopy(parent2)
def mutate(self, individual, building, energy_system):
"""
Mutates the individual's generation and storage components.
- `individual`: The individual to mutate (contains generation and storage components).
- `building`: Building data that contains constraints such as peak heating load and available space.
Returns the mutated individual.
"""
design_period_energy_demands = self.design_period_identification(building)
# Mutate Generation Components
for generation_component in individual['Generation Components']:
if random.random() < self.mutation_rate:
if (generation_component['nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in
energy_system.demand_types):
# Mutate heating capacity
if cte.HEATING in energy_system.demand_types:
generation_component['heating_capacity'] = random.uniform(
0, max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
generation_component['heating_capacity'] = random.uniform(
0, max(design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in energy_system.demand_types:
# Mutate cooling capacity
generation_component['cooling_capacity'] = random.uniform(
0, max(design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
# Mutate storage Components
for storage_component in individual['Energy Storage Components']:
if random.random() < self.mutation_rate:
if storage_component['type'] == f'{cte.THERMAL}_storage':
# Mutate the volume of thermal storage
max_available_space = 0.01 * building.volume / building.storeys_above_ground
storage_component['volume'] = random.uniform(0, max_available_space)
if storage_component['heating_coil_capacity'] is not None:
if cte.HEATING in energy_system.demand_types:
storage_component['heating_coil_capacity'] = random.uniform(0, max(
design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
else:
storage_component['heating_coil_capacity'] = random.uniform(0, max(
design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
return individual
def solve_ga(self, building, energy_system):
"""
solving GA for a single energy system. Here are the steps:
1. Initialize population using the "initialize_population" method in this class.
2. Evaluate the initial population using the "score_evaluation" method in the Individual class.
3. sort population based on fitness score.
4. Repeat selection, crossover, and mutation for a fixed number of generations.
5. Track the best solution found during the optimization process.
:param building: Building object for the energy system.
:param energy_system: Energy system to optimize.
:return: Best solution after running the GA.
"""
# step 1: Initialize the population
self.initialize_population(building, energy_system)
# step 2: Evaluate the initial population
for individual in self.population:
individual.score_evaluation()
# step 3: Order population based on fitness scores
self.order_population()
print([individual.fitness_score for individual in self.population])
# Track the best solution
self.best_solution = self.population[0]
self.best_solution_generation = 0
self.list_of_solutions.append(copy.deepcopy(self.best_solution.individual))
# step 4: Run GA for a fixed number of generations
for generation in range(1, self.generations):
print(f"Generation {generation}")
# selection (using tournament selection)
selected_population = self.tournament_selection()
# Create the next generation through crossover and mutation
next_population = []
for i in range(0, self.population_size, 2):
parent1 = selected_population[i]
parent2 = selected_population[i + 1] if (i + 1) < len(selected_population) else selected_population[0]
# step 5: Apply crossover
child1, child2 = self.crossover(parent1, parent2)
# step 6: Apply mutation
self.mutate(child1.individual, building, energy_system)
self.mutate(child2.individual, building, energy_system)
# step 7: Evaluate the children
child1.score_evaluation()
child2.score_evaluation()
next_population.extend([child1, child2])
# Replace old population with the new one
self.population = next_population
# step 8: sort the new population based on fitness
self.order_population()
print([individual.fitness_score for individual in self.population])
# Track the best solution found in this generation
if self.population[0].individual['fitness_score'] < self.best_solution.individual['fitness_score']:
self.best_solution = self.population[0]
self.best_solution_generation = generation
# store the best solution in the list of solutions
self.list_of_solutions.append(copy.deepcopy(self.population[0].individual))
print(f"Best solution found in generation {self.best_solution_generation}")
print(f"Best solution: {self.best_solution.individual}")
return self.best_solution
@staticmethod
def topsis_decision_making(pareto_front):
"""
Perform TOPSIS decision-making to choose the best solution from the Pareto front.
:param pareto_front: List of individuals in the Pareto front
:return: The best individual based on TOPSIS ranking
"""
# Step 1: Normalize the objective functions (cost and energy consumption)
min_lcc = min([ind.individual['lcc'] for ind in pareto_front])
max_lcc = max([ind.individual['lcc'] for ind in pareto_front])
min_lce = min([ind.individual['total_energy_consumption'] for ind in pareto_front])
max_lce = max([ind.individual['total_energy_consumption'] for ind in pareto_front])
normalized_pareto_front = []
for ind in pareto_front:
normalized_lcc = (ind.individual['lcc'] - min_lcc) / (max_lcc - min_lcc) if max_lcc > min_lcc else 0
normalized_lce = (ind.individual['total_energy_consumption'] - min_lce) / (
max_lce - min_lce) if max_lce > min_lce else 0
normalized_pareto_front.append((ind, normalized_lcc, normalized_lce))
# Step 2: Calculate the ideal and worst solutions
ideal_solution = (0, 0) # Ideal is minimum LCC and minimum LCE (0, 0 after normalization)
worst_solution = (1, 1) # Worst is maximum LCC and maximum LCE (1, 1 after normalization)
# Step 3: Calculate the distance to the ideal and worst solutions
best_distances = []
worst_distances = []
for ind, normalized_lcc, normalized_lce in normalized_pareto_front:
distance_to_ideal = math.sqrt(
(normalized_lcc - ideal_solution[0]) ** 2 + (normalized_lce - ideal_solution[1]) ** 2)
distance_to_worst = math.sqrt(
(normalized_lcc - worst_solution[0]) ** 2 + (normalized_lce - worst_solution[1]) ** 2)
best_distances.append(distance_to_ideal)
worst_distances.append(distance_to_worst)
# Step 4: Calculate relative closeness to the ideal solution
similarity = [worst / (best + worst) for best, worst in zip(best_distances, worst_distances)]
# Step 5: Select the individual with the highest similarity score
best_index = similarity.index(max(similarity))
best_solution = pareto_front[best_index]
return best_solution
@staticmethod
def design_period_identification(building):
def get_start_end_indices(max_day_index, total_days):
if max_day_index > 0 and max_day_index < total_days - 1:
start_index = (max_day_index - 1) * 24
end_index = (max_day_index + 2) * 24
elif max_day_index == 0:
start_index = 0
end_index = (max_day_index + 2) * 24
else:
start_index = (max_day_index - 1) * 24
end_index = total_days * 24
return start_index, end_index
# Calculate daily demands
heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.heating_demand[cte.HOUR]), 24)]
cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.cooling_demand[cte.HOUR]), 24)]
dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
# Get the day with maximum demand for each type
heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
# Get the start and end indices for each demand type
heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
# Return the design period energy demands
return {
f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
'start_index': heating_start, 'end_index': heating_end},
f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
'start_index': cooling_start, 'end_index': cooling_end},
f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
'start_index': dhw_start, 'end_index': dhw_end}
}

42
energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/optimal_sizing.py
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import hub.helpers.constants as cte
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.single_objective_genetic_algorithm import \
SingleObjectiveGeneticAlgorithm
class OptimalSizing:
def __init__(self, city, optimization_scenario):
self.city = city
self.optimization_scenario = optimization_scenario
def enrich_buildings(self):
for building in self.city.buildings:
for energy_system in building.energy_systems:
if len(energy_system.generation_systems) == 1 and energy_system.generation_systems[0].energy_storage_systems is None:
if energy_system.generation_systems[0].system_type == cte.PHOTOVOLTAIC:
pass
else:
if cte.HEATING in energy_system.demand_types:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
design_load = max([building.heating_demand[cte.HOUR][i] +
building.domestic_hot_water_heat_demand[cte.HOUR][i] for i in
range(len(building.heating_demand))]) / cte.WATTS_HOUR_TO_JULES
else:
design_load = building.heating_peak_load[cte.YEAR][0]
energy_system.generation_systems[0].nominal_heat_output = design_load
elif cte.COOLING in energy_system.demand_types:
energy_system.generation_systems[0].nominal_cooling_output = building.cooling_peak_load[cte.YEAR][0]
else:
optimized_system = SingleObjectiveGeneticAlgorithm(optimization_scenario=self.optimization_scenario).solve_ga(building, energy_system)
for generation_system in energy_system.generation_systems:
system_type = generation_system.system_type
for generation_component in optimized_system.individual['Generation Components']:
if generation_component['type'] == system_type:
generation_system.nominal_heat_output = generation_component['heating_capacity']
generation_system.nominal_cooling_output = generation_component['cooling_capacity']
if generation_system.energy_storage_systems is not None:
for storage_system in generation_system.energy_storage_systems:
storage_type = f'{storage_system.type_energy_stored}_storage'
for storage_component in optimized_system.individual['Energy Storage Components']:
if storage_component['type'] == storage_type:
storage_system.nominal_capacity = storage_component['capacity']
storage_system.volume = storage_component['volume']

99
energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/peak_load_sizing.py
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@ -1,99 +0,0 @@
import hub.helpers.constants as cte
class PeakLoadSizing:
def __init__(self, city, default_primary_unit_percentage=0.7, storage_peak_coverage=3):
self.city = city
self.default_primary_unit_percentage = default_primary_unit_percentage
self.storage_peak_coverage = storage_peak_coverage
def enrich_buildings(self):
total_demand = 0
for building in self.city.buildings:
energy_systems = building.energy_systems
for energy_system in energy_systems:
if cte.HEATING in energy_system.demand_types:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
total_demand = [(building.heating_demand[cte.HOUR][i] +
building.domestic_hot_water_heat_demand[cte.HOUR][i]) / cte.WATTS_HOUR_TO_JULES
for i in range(len(building.heating_demand[cte.HOUR]))]
else:
total_demand = building.heating_peak_load[cte.YEAR]
design_load = max(total_demand)
self.allocate_capacity(energy_system, design_load, cte.HEATING, self.default_primary_unit_percentage)
if cte.COOLING in energy_system.demand_types:
cooling_design_load = building.cooling_peak_load[cte.YEAR][0]
self.allocate_capacity(energy_system, cooling_design_load, cte.COOLING,
self.default_primary_unit_percentage)
elif cte.COOLING in energy_system.demand_types:
design_load = building.cooling_peak_load[cte.YEAR][0]
self.allocate_capacity(energy_system, design_load, cte.COOLING, self.default_primary_unit_percentage)
elif cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
design_load = building.domestic_hot_water_peak_load[cte.YEAR][0]
self.allocate_capacity(energy_system, design_load, cte.DOMESTIC_HOT_WATER,
self.default_primary_unit_percentage)
for generation_system in energy_system.generation_systems:
storage_systems = generation_system.energy_storage_systems
if storage_systems is not None:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
operation_range = 10
else:
operation_range = 20
for storage_system in storage_systems:
if storage_system.type_energy_stored == cte.THERMAL:
self.tes_sizing(storage_system, max(total_demand), self.storage_peak_coverage, operation_range)
def allocate_capacity(self, energy_system, design_load, demand_type, default_primary_unit_percentage):
if len(energy_system.generation_systems) == 1:
# If there's only one generation system, it gets the full design load.
if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
energy_system.generation_systems[0].nominal_heat_output = design_load
elif demand_type == cte.COOLING:
energy_system.generation_systems[0].nominal_cooling_output = design_load
else:
cooling_equipments_number = 0
# Distribute the load among generation systems.
max_efficiency = 0
main_generation_unit = None
for generation_system in energy_system.generation_systems:
if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
if max_efficiency < float(generation_system.heat_efficiency):
max_efficiency = float(generation_system.heat_efficiency)
main_generation_unit = generation_system
elif demand_type == cte.COOLING and generation_system.fuel_type == cte.ELECTRICITY:
cooling_equipments_number += 1
if max_efficiency < float(generation_system.cooling_efficiency):
max_efficiency = float(generation_system.heat_efficiency)
main_generation_unit = generation_system
for generation_system in energy_system.generation_systems:
if generation_system.system_type == main_generation_unit.system_type:
if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
generation_system.nominal_heat_output = round(default_primary_unit_percentage * design_load)
elif demand_type == cte.COOLING and cooling_equipments_number > 1:
generation_system.nominal_cooling_output = round(default_primary_unit_percentage * design_load)
else:
generation_system.nominal_cooling_output = design_load
else:
if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
generation_system.nominal_heat_output = round(((1 - default_primary_unit_percentage) * design_load /
(len(energy_system.generation_systems) - 1)))
elif demand_type == cte.COOLING and cooling_equipments_number > 1:
generation_system.nominal_cooling_output = round(((1 - default_primary_unit_percentage) * design_load /
(len(energy_system.generation_systems) - 1)))
@staticmethod
def tes_sizing(storage, peak_load, coverage, operational_temperature_range):
storage.volume = round((peak_load * coverage * cte.WATTS_HOUR_TO_JULES) /
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * operational_temperature_range))
@staticmethod
def cooling_equipments(energy_system):
counter = 0
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
counter += 1
return counter

595
energy_system_modelling_package/energy_system_retrofit/energy_system_retrofit_report.py
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@ -1,595 +0,0 @@
import os
import hub.helpers.constants as cte
import matplotlib.pyplot as plt
from matplotlib import cm
from energy_system_modelling_package.report_creation import LatexReport
from matplotlib.ticker import MaxNLocator
import numpy as np
from pathlib import Path
import glob
class EnergySystemRetrofitReport:
def __init__(self, city, output_path, retrofit_scenario, current_status_energy_consumption_data,
retrofitted_energy_consumption_data, current_status_lcc_data, retrofitted_lcc_data):
self.city = city
self.current_status_data = current_status_energy_consumption_data
self.retrofitted_data = retrofitted_energy_consumption_data
self.current_status_lcc = current_status_lcc_data
self.retrofitted_lcc = retrofitted_lcc_data
self.output_path = output_path
self.content = []
self.retrofit_scenario = retrofit_scenario
self.report = LatexReport('energy_system_retrofit_report',
'Energy System Retrofit Report', self.retrofit_scenario, output_path)
self.system_schemas_path = (Path(__file__).parent.parent.parent / 'hub' / 'data' / 'energy_systems' / 'schemas')
self.charts_path = Path(output_path) / 'charts'
self.charts_path.mkdir(parents=True, exist_ok=True)
files = glob.glob(f'{self.charts_path}/*')
for file in files:
os.remove(file)
def building_energy_info(self):
table_data = [
["Building Name", "Year of Construction", "function", "Yearly Heating Demand (MWh)",
"Yearly Cooling Demand (MWh)", "Yearly DHW Demand (MWh)", "Yearly Electricity Demand (MWh)"]
]
intensity_table_data = [["Building Name", "Total Floor Area $m^2$", "Heating Demand Intensity kWh/ $m^2$",
"Cooling Demand Intensity kWh/ $m^2$", "Electricity Intensity kWh/ $m^2$"]]
peak_load_data = [["Building Name", "Heating Peak Load (kW)", "Cooling Peak Load (kW)",
"Domestic Hot Water Peak Load (kW)"]]
for building in self.city.buildings:
total_floor_area = 0
for zone in building.thermal_zones_from_internal_zones:
total_floor_area += zone.total_floor_area
building_data = [
building.name,
str(building.year_of_construction),
building.function,
str(format(building.heating_demand[cte.YEAR][0] / 3.6e9, '.2f')),
str(format(building.cooling_demand[cte.YEAR][0] / 3.6e9, '.2f')),
str(format(building.domestic_hot_water_heat_demand[cte.YEAR][0] / 3.6e9, '.2f')),
str(format(
(building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0])
/ 3.6e9, '.2f')),
]
intensity_data = [
building.name,
str(format(total_floor_area, '.2f')),
str(format(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
str(format(building.cooling_demand[cte.YEAR][0] / (3.6e6 * total_floor_area), '.2f')),
str(format(
(building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
(3.6e6 * total_floor_area), '.2f'))
]
peak_data = [
building.name,
str(format(building.heating_peak_load[cte.YEAR][0] / 1000, '.2f')),
str(format(building.cooling_peak_load[cte.YEAR][0] / 1000, '.2f')),
str(format(
(building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
(3.6e6 * total_floor_area), '.2f'))
]
table_data.append(building_data)
intensity_table_data.append(intensity_data)
peak_load_data.append(peak_data)
self.report.add_table(table_data, caption='Buildings Energy Consumption Data')
self.report.add_table(intensity_table_data, caption='Buildings Energy Use Intensity Data')
self.report.add_table(peak_load_data, caption='Buildings Peak Load Data')
def plot_monthly_energy_demands(self, data, file_name, title):
# Data preparation
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
demands = {
'Heating': ('heating', '#2196f3'),
'Cooling': ('cooling', '#ff5a5f'),
'DHW': ('dhw', '#4caf50'),
'Electricity': ('lighting_appliance', '#ffc107')
}
# Helper function for plotting
def plot_bar_chart(ax, demand_type, color, ylabel, title):
values = data[demand_type]
ax.bar(months, values, color=color, width=0.6, zorder=2)
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.set_xticks(np.arange(len(months)))
ax.set_xticklabels(months, rotation=45, ha='right')
ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
ax.spines[['top', 'left', 'bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)
average_value = np.mean(values)
ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
color='grey')
# Plotting
fig, axs = plt.subplots(4, 1, figsize=(20, 16), dpi=96)
fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
plot_bar_chart(axs[0], 'heating', demands['Heating'][1], 'Heating Demand (kWh)', 'Monthly Heating Demand')
plot_bar_chart(axs[1], 'cooling', demands['Cooling'][1], 'Cooling Demand (kWh)', 'Monthly Cooling Demand')
plot_bar_chart(axs[2], 'dhw', demands['DHW'][1], 'DHW Demand (kWh)', 'Monthly DHW Demand')
plot_bar_chart(axs[3], 'lighting_appliance', demands['Electricity'][1], 'Electricity Demand (kWh)',
'Monthly Electricity Demand')
# Set a white background
fig.patch.set_facecolor('white')
# Adjust the margins around the plot area
plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, hspace=0.5)
# Save the plot
chart_path = self.charts_path / f'{file_name}.png'
plt.savefig(chart_path, bbox_inches='tight')
plt.close()
return chart_path
def plot_monthly_energy_consumption(self, data, file_name, title):
# Data preparation
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
consumptions = {
'Heating': ('heating', '#2196f3', 'Heating Consumption (kWh)', 'Monthly Energy Consumption for Heating'),
'Cooling': ('cooling', '#ff5a5f', 'Cooling Consumption (kWh)', 'Monthly Energy Consumption for Cooling'),
'DHW': ('dhw', '#4caf50', 'DHW Consumption (kWh)', 'Monthly DHW Consumption')
}
# Helper function for plotting
def plot_bar_chart(ax, consumption_type, color, ylabel, title):
values = data[consumption_type]
ax.bar(months, values, color=color, width=0.6, zorder=2)
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
ax.set_xlabel('Month', fontsize=12, labelpad=10)
ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.set_xticks(np.arange(len(months)))
ax.set_xticklabels(months, rotation=45, ha='right')
ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
ax.spines[['top', 'left', 'bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)
average_value = np.mean(values)
ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
color='grey')
# Plotting
fig, axs = plt.subplots(3, 1, figsize=(20, 15), dpi=96)
fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
plot_bar_chart(axs[0], 'heating', consumptions['Heating'][1], consumptions['Heating'][2],
consumptions['Heating'][3])
plot_bar_chart(axs[1], 'cooling', consumptions['Cooling'][1], consumptions['Cooling'][2],
consumptions['Cooling'][3])
plot_bar_chart(axs[2], 'dhw', consumptions['DHW'][1], consumptions['DHW'][2], consumptions['DHW'][3])
# Set a white background
fig.patch.set_facecolor('white')
# Adjust the margins around the plot area
plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, wspace=0.3, hspace=0.5)
# Save the plot
chart_path = self.charts_path / f'{file_name}.png'
plt.savefig(chart_path, bbox_inches='tight')
plt.close()
return chart_path
def fuel_consumption_breakdown(self, file_name, data):
fuel_consumption_breakdown = {}
for building in self.city.buildings:
for key, breakdown in data[f'{building.name}']['energy_consumption_breakdown'].items():
if key not in fuel_consumption_breakdown:
fuel_consumption_breakdown[key] = {sector: 0 for sector in breakdown}
for sector, value in breakdown.items():
if sector in fuel_consumption_breakdown[key]:
fuel_consumption_breakdown[key][sector] += value / 3.6e6
else:
fuel_consumption_breakdown[key][sector] = value / 3.6e6
plt.style.use('ggplot')
num_keys = len(fuel_consumption_breakdown)
fig, axs = plt.subplots(1 if num_keys <= 2 else num_keys, min(num_keys, 2), figsize=(12, 5))
axs = axs if num_keys > 1 else [axs] # Ensure axs is always iterable
for i, (fuel, breakdown) in enumerate(fuel_consumption_breakdown.items()):
labels = breakdown.keys()
values = breakdown.values()
colors = cm.get_cmap('tab20c', len(labels))
ax = axs[i] if num_keys > 1 else axs[0]
ax.pie(values, labels=labels,
autopct=lambda pct: f"{pct:.1f}%\n({pct / 100 * sum(values):.2f})",
startangle=90, colors=[colors(j) for j in range(len(labels))])
ax.set_title(f'{fuel} Consumption Breakdown')
plt.suptitle('City Energy Consumption Breakdown', fontsize=16, fontweight='bold')
plt.tight_layout(rect=[0, 0, 1, 0.95]) # Adjust layout to fit the suptitle
chart_path = self.charts_path / f'{file_name}.png'
plt.savefig(chart_path, dpi=300)
plt.close()
return chart_path
def energy_system_archetype_schematic(self):
energy_system_archetypes = {}
for building in self.city.buildings:
if building.energy_systems_archetype_name not in energy_system_archetypes:
energy_system_archetypes[building.energy_systems_archetype_name] = [building.name]
else:
energy_system_archetypes[building.energy_systems_archetype_name].append(building.name)
text = ""
items = ""
for archetype, buildings in energy_system_archetypes.items():
buildings_str = ", ".join(buildings)
text += f"Figure 4 shows the schematic of the proposed energy system for buildings {buildings_str}.\n"
if archetype in ['PV+4Pipe+DHW', 'PV+ASHP+GasBoiler+TES', 'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating and PV']:
text += "This energy system archetype is formed of the following systems: \par"
items = ['Rooftop Photovoltaic System: The rooftop PV system is tied to the grid and in case there is surplus '
'energy, it is sold to Hydro-Quebec through their Net-Meterin program.',
'4-Pipe HVAC System: This systems includes a 4-pipe heat pump capable of generating heat and cooling '
'at the same time, a natural gas boiler as the auxiliary heating system, and a hot water storage tank.'
'The temperature inside the tank is kept between 40-55 C. The cooling demand is totally supplied by '
'the heat pump unit.',
'Domestic Hot Water Heat Pump System: This system is in charge of supplying domestic hot water demand.'
'The heat pump is connected to a thermal storage tank with electric resistance heating coil inside it.'
' The temperature inside the tank should always remain above 60 C.']
self.report.add_text(text)
self.report.add_itemize(items=items)
schema_path = self.system_schemas_path / f'{archetype}.jpg'
self.report.add_image(str(schema_path).replace('\\', '/'),
f'Proposed energy system for buildings {buildings_str}')
def plot_monthly_radiation(self):
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
monthly_roof_radiation = []
for i in range(len(months)):
tilted_radiation = 0
for building in self.city.buildings:
tilted_radiation += (building.roofs[0].global_irradiance_tilted[cte.MONTH][i] / 1000)
monthly_roof_radiation.append(tilted_radiation)
def plot_bar_chart(ax, months, values, color, ylabel, title):
ax.bar(months, values, color=color, width=0.6, zorder=2)
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
ax.set_xlabel('Month', fontsize=12, labelpad=10)
ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.set_xticks(np.arange(len(months)))
ax.set_xticklabels(months, rotation=45, ha='right')
ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
ax.spines[['top', 'left', 'bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)
average_value = np.mean(values)
ax.axhline(y=average_value, color='grey', linewidth=2, linestyle='--')
ax.text(len(months) - 1, average_value, f'Average = {average_value:.1f} kWh', ha='right', va='bottom',
color='grey')
# Plotting the bar chart
fig, ax = plt.subplots(figsize=(15, 8), dpi=96)
plot_bar_chart(ax, months, monthly_roof_radiation, '#ffc107', 'Tilted Roof Radiation (kWh / m2)',
'Monthly Tilted Roof Radiation')
# Set a white background
fig.patch.set_facecolor('white')
# Adjust the margins around the plot area
plt.subplots_adjust(left=0.1, right=0.95, top=0.9, bottom=0.1)
# Save the plot
chart_path = self.charts_path / 'monthly_tilted_roof_radiation.png'
plt.savefig(chart_path, bbox_inches='tight')
plt.close()
return chart_path
def energy_consumption_comparison(self, current_status_data, retrofitted_data, file_name, title):
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
consumptions = {
'Heating': ('heating', '#2196f3', 'Heating Consumption (kWh)', 'Monthly Energy Consumption for Heating'),
'Cooling': ('cooling', '#ff5a5f', 'Cooling Consumption (kWh)', 'Monthly Energy Consumption for Cooling'),
'DHW': ('dhw', '#4caf50', 'DHW Consumption (kWh)', 'Monthly DHW Consumption')
}
# Helper function for plotting
def plot_double_bar_chart(ax, consumption_type, color, ylabel, title):
current_values = current_status_data[consumption_type]
retrofitted_values = retrofitted_data[consumption_type]
bar_width = 0.35
index = np.arange(len(months))
ax.bar(index, current_values, bar_width, label='Current Status', color=color, alpha=0.7, zorder=2)
ax.bar(index + bar_width, retrofitted_values, bar_width, label='Retrofitted', color=color, hatch='/', zorder=2)
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
ax.set_xlabel('Month', fontsize=12, labelpad=10)
ax.set_ylabel(ylabel, fontsize=14, labelpad=10)
ax.set_title(title, fontsize=14, weight='bold', alpha=.8, pad=40)
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(months, rotation=45, ha='right')
ax.legend()
# Adding bar labels
ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=90)
ax.bar_label(ax.containers[1], padding=3, color='black', fontsize=12, rotation=90)
ax.spines[['top', 'left', 'bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)
# Plotting
fig, axs = plt.subplots(3, 1, figsize=(20, 25), dpi=96)
fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
plot_double_bar_chart(axs[0], 'heating', consumptions['Heating'][1], consumptions['Heating'][2],
consumptions['Heating'][3])
plot_double_bar_chart(axs[1], 'cooling', consumptions['Cooling'][1], consumptions['Cooling'][2],
consumptions['Cooling'][3])
plot_double_bar_chart(axs[2], 'dhw', consumptions['DHW'][1], consumptions['DHW'][2], consumptions['DHW'][3])
# Set a white background
fig.patch.set_facecolor('white')
# Adjust the margins around the plot area
plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.1, wspace=0.3, hspace=0.5)
# Save the plot
chart_path = self.charts_path / f'{file_name}.png'
plt.savefig(chart_path, bbox_inches='tight')
plt.close()
return chart_path
def yearly_consumption_comparison(self):
current_total_consumption = round(self.current_status_data['total_consumption'], 2)
retrofitted_total_consumption = round(self.retrofitted_data['total_consumption'], 2)
text = (
f'The total yearly energy consumption before and after the retrofit are {current_total_consumption} MWh and '
f'{retrofitted_total_consumption} MWh, respectively.')
if retrofitted_total_consumption < current_total_consumption:
change = str(round((current_total_consumption - retrofitted_total_consumption) * 100 / current_total_consumption,
2))
text += f'Therefore, the total yearly energy consumption decreased by {change} \%.'
else:
change = str(round((retrofitted_total_consumption - current_total_consumption) * 100 /
retrofitted_total_consumption, 2))
text += f'Therefore, the total yearly energy consumption increased by {change} \%. \par'
self.report.add_text(text)
def pv_system(self):
self.report.add_text('The first step in PV assessments is evaluating the potential of buildings for installing '
'rooftop PV system. The benchmark value used for this evaluation is the mean yearly solar '
'incident in Montreal. According to Hydro-Quebec, the mean annual incident in Montreal is 1350'
'kWh/m2. Therefore, any building with rooftop annual global horizontal radiation of less than '
'1080 kWh/m2 is considered to be infeasible. Table 4 shows the yearly horizontal radiation on '
'buildings roofs. \par')
radiation_data = [
["Building Name", "Roof Area $m^2$", "Function", "Rooftop Annual Global Horizontal Radiation kWh/ $m^2$"]
]
pv_feasible_buildings = []
for building in self.city.buildings:
if building.roofs[0].global_irradiance[cte.YEAR][0] > 1080:
pv_feasible_buildings.append(building.name)
data = [building.name, str(format(building.roofs[0].perimeter_area, '.2f')), building.function,
str(format(building.roofs[0].global_irradiance[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000), '.2f'))]
radiation_data.append(data)
self.report.add_table(radiation_data,
caption='Buildings Roof Characteristics')
if len(pv_feasible_buildings) == len(self.city.buildings):
buildings_str = 'all'
else:
buildings_str = ", ".join(pv_feasible_buildings)
self.report.add_text(f"From the table it can be seen that {buildings_str} buildings are good candidates to have "
f"rooftop PV system. The next step is calculating the amount of solar radiation on a tilted "
f"surface. Figure 5 shows the total monthly solar radiation on a surface with the tilt angle "
f"of 45 degrees on the roofs of those buildings that are identified to have rooftop PV system."
f"\par")
tilted_radiation = self.plot_monthly_radiation()
self.report.add_image(str(tilted_radiation).replace('\\', '/'),
caption='Total Monthly Solar Radiation on Buildings Roofs on a 45 Degrees Tilted Surface',
placement='H')
self.report.add_text('The first step in sizing the PV system is to find the available roof area. '
'Few considerations need to be made here. The considerations include space for maintenance '
'crew, space for mechanical equipment, and orientation correction factor to make sure all '
'the panel are truly facing south. After all these considerations, the minimum distance '
'between the panels to avoid shading throughout the year is found. Table 5 shows the number of'
'panles on each buildings roof, yearly PV production, total electricity consumption, and self '
'consumption. \par')
pv_output_table = [['Building Name', 'Total Surface Area of PV Panels ($m^2$)',
'Total Solar Incident on PV Modules (MWh)', 'Yearly PV production (MWh)']]
for building in self.city.buildings:
if building.name in pv_feasible_buildings:
pv_data = []
pv_data.append(building.name)
yearly_solar_incident = (building.roofs[0].global_irradiance_tilted[cte.YEAR][0] *
building.roofs[0].installed_solar_collector_area) / (cte.WATTS_HOUR_TO_JULES * 1e6)
yearly_solar_incident_str = format(yearly_solar_incident, '.2f')
yearly_pv_output = building.onsite_electrical_production[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1e6)
yearly_pv_output_str = format(yearly_pv_output, '.2f')
pv_data.append(format(building.roofs[0].installed_solar_collector_area, '.2f'))
pv_data.append(yearly_solar_incident_str)
pv_data.append(yearly_pv_output_str)
pv_output_table.append(pv_data)
self.report.add_table(pv_output_table, caption='PV System Simulation Results', first_column_width=3)
def life_cycle_cost_stacked_bar(self, file_name, title):
# Aggregate LCC components for current and retrofitted statuses
current_status_capex = 0
current_status_opex = 0
current_status_maintenance = 0
current_status_end_of_life = 0
retrofitted_capex = 0
retrofitted_opex = 0
retrofitted_maintenance = 0
retrofitted_end_of_life = 0
current_status_operational_income = 0
retrofitted_operational_income = 0
for building in self.city.buildings:
current_status_capex += self.current_status_lcc[f'{building.name}']['capital_cost_per_sqm']
retrofitted_capex += self.retrofitted_lcc[f'{building.name}']['capital_cost_per_sqm']
current_status_opex += self.current_status_lcc[f'{building.name}']['operational_cost_per_sqm']
retrofitted_opex += self.retrofitted_lcc[f'{building.name}']['operational_cost_per_sqm']
current_status_maintenance += self.current_status_lcc[f'{building.name}']['maintenance_cost_per_sqm']
retrofitted_maintenance += self.retrofitted_lcc[f'{building.name}']['maintenance_cost_per_sqm']
current_status_end_of_life += self.current_status_lcc[f'{building.name}']['end_of_life_cost_per_sqm']
retrofitted_end_of_life += self.retrofitted_lcc[f'{building.name}']['end_of_life_cost_per_sqm']
current_status_operational_income += self.current_status_lcc[f'{building.name}']['operational_income_per_sqm']
retrofitted_operational_income += self.retrofitted_lcc[f'{building.name}']['operational_income_per_sqm']
current_status_lcc_components_sqm = {
'Capital Cost': current_status_capex / len(self.city.buildings),
'Operational Cost': (current_status_opex - current_status_operational_income) / len(self.city.buildings),
'Maintenance Cost': current_status_maintenance / len(self.city.buildings),
'End of Life Cost': current_status_end_of_life / len(self.city.buildings),
}
retrofitted_lcc_components_sqm = {
'Capital Cost': retrofitted_capex / len(self.city.buildings),
'Operational Cost': (retrofitted_opex - retrofitted_operational_income) / len(self.city.buildings),
'Maintenance Cost': retrofitted_maintenance / len(self.city.buildings),
'End of Life Cost': retrofitted_end_of_life / len(self.city.buildings),
}
labels = ['Current Status', 'Retrofitted Status']
categories = ['Capital Cost', 'Operational Cost', 'Maintenance Cost', 'End of Life Cost']
colors = ['#2196f3', '#ff5a5f', '#4caf50', '#ffc107'] # Added new color
# Data preparation
bar_width = 0.35
r = np.arange(len(labels))
fig, ax = plt.subplots(figsize=(12, 8), dpi=96)
fig.suptitle(title, fontsize=16, weight='bold', alpha=.8)
# Plotting current status data
bottom = np.zeros(len(labels))
for category, color in zip(categories, colors):
values = [current_status_lcc_components_sqm[category], retrofitted_lcc_components_sqm[category]]
ax.bar(r, values, bottom=bottom, color=color, edgecolor='white', width=bar_width, label=category)
bottom += values
# Adding summation annotations at the top of the bars
for idx, (x, total) in enumerate(zip(r, bottom)):
ax.text(x, total, f'{total:.1f}', ha='center', va='bottom', fontsize=12, fontweight='bold')
# Adding labels, title, and grid
ax.set_xlabel('LCC Components', fontsize=12, labelpad=10)
ax.set_ylabel('Average Cost (CAD/m²)', fontsize=14, labelpad=10)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
ax.set_xticks(r)
ax.set_xticklabels(labels, rotation=45, ha='right')
ax.legend()
# Adding a white background
fig.patch.set_facecolor('white')
# Adjusting the margins around the plot area
plt.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.2)
# Save the plot
chart_path = self.charts_path / f'{file_name}.png'
plt.savefig(chart_path, bbox_inches='tight')
plt.close()
return chart_path
def create_report(self):
# Add sections and text to the report
self.report.add_section('Overview of the Current Status in Buildings')
self.report.add_text('In this section, an overview of the current status of buildings characteristics, '
'energy demand and consumptions are provided')
self.report.add_subsection('Buildings Characteristics')
self.building_energy_info()
# current monthly demands and consumptions
current_monthly_demands = self.current_status_data['monthly_demands']
current_monthly_consumptions = self.current_status_data['monthly_consumptions']
# Plot and save demand chart
current_demand_chart_path = self.plot_monthly_energy_demands(data=current_monthly_demands,
file_name='current_monthly_demands',
title='Current Status Monthly Energy Demands')
# Plot and save consumption chart
current_consumption_chart_path = self.plot_monthly_energy_consumption(data=current_monthly_consumptions,
file_name='monthly_consumptions',
title='Monthly Energy Consumptions')
current_consumption_breakdown_path = self.fuel_consumption_breakdown('City_Energy_Consumption_Breakdown',
self.current_status_data)
retrofitted_consumption_breakdown_path = self.fuel_consumption_breakdown(
'fuel_consumption_breakdown_after_retrofit',
self.retrofitted_data)
life_cycle_cost_sqm_stacked_bar_chart_path = self.life_cycle_cost_stacked_bar('lcc_per_sqm',
'LCC Analysis')
# Add current state of energy demands in the city
self.report.add_subsection('Current State of Energy Demands in the City')
self.report.add_text('The total monthly energy demands in the city are shown in Figure 1. It should be noted '
'that the electricity demand refers to total lighting and appliance electricity demands')
self.report.add_image(str(current_demand_chart_path).replace('\\', '/'),
'Total Monthly Energy Demands in City',
placement='h')
# Add current state of energy consumption in the city
self.report.add_subsection('Current State of Energy Consumption in the City')
self.report.add_text('The following figure shows the amount of energy consumed to supply heating, cooling, and '
'domestic hot water needs in the city. The details of the systems in each building before '
'and after retrofit are provided in Section 4. \par')
self.report.add_image(str(current_consumption_chart_path).replace('\\', '/'),
'Total Monthly Energy Consumptions in City',
placement='H')
self.report.add_text('Figure 3 shows the yearly energy supplied to the city by fuel in different sectors. '
'All the values are in kWh.')
self.report.add_image(str(current_consumption_breakdown_path).replace('\\', '/'),
'Current Energy Consumption Breakdown in the City by Fuel',
placement='H')
self.report.add_section(f'{self.retrofit_scenario}')
self.report.add_subsection('Proposed Systems')
self.energy_system_archetype_schematic()
if 'PV' in self.retrofit_scenario:
self.report.add_subsection('Rooftop Photovoltaic System Implementation')
self.pv_system()
if 'System' in self.retrofit_scenario:
self.report.add_subsection('Retrofitted HVAC and DHW Systems')
self.report.add_text('Figure 6 shows a comparison between total monthly energy consumption in the selected '
'buildings before and after retrofitting.')
consumption_comparison = self.energy_consumption_comparison(self.current_status_data['monthly_consumptions'],
self.retrofitted_data['monthly_consumptions'],
'energy_consumption_comparison_in_city',
'Total Monthly Energy Consumption Comparison in '
'Buildings')
self.report.add_image(str(consumption_comparison).replace('\\', '/'),
caption='Comparison of Total Monthly Energy Consumption in City Buildings',
placement='H')
self.yearly_consumption_comparison()
self.report.add_text('Figure 7 shows the fuel consumption breakdown in the area after the retrofit.')
self.report.add_image(str(retrofitted_consumption_breakdown_path).replace('\\', '/'),
caption=f'Fuel Consumption Breakdown After {self.retrofit_scenario}',
placement='H')
self.report.add_subsection('Life Cycle Cost Analysis')
self.report.add_image(str(life_cycle_cost_sqm_stacked_bar_chart_path).replace('\\', '/'),
caption='Average Life Cycle Cost Components',
placement='H')
# Save and compile the report
self.report.save_report()
self.report.compile_to_pdf()

176
energy_system_modelling_package/energy_system_retrofit/energy_system_retrofit_results.py
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@ -1,176 +0,0 @@
import hub.helpers.constants as cte
def hourly_electricity_consumption_profile(building):
hourly_electricity_consumption = []
energy_systems = building.energy_systems
appliance = building.appliances_electrical_demand[cte.HOUR]
lighting = building.lighting_electrical_demand[cte.HOUR]
elec_heating = 0
elec_cooling = 0
elec_dhw = 0
if cte.HEATING in building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_heating = 1
if cte.COOLING in building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_cooling = 1
if cte.DOMESTIC_HOT_WATER in building.energy_consumption_breakdown[cte.ELECTRICITY]:
elec_dhw = 1
heating = None
cooling = None
dhw = None
if elec_heating == 1:
for energy_system in energy_systems:
if cte.HEATING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if cte.HEATING in generation_system.energy_consumption:
heating = generation_system.energy_consumption[cte.HEATING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
heating = [x / 2 for x in building.heating_consumption[cte.HOUR]]
else:
heating = building.heating_consumption[cte.HOUR]
if elec_dhw == 1:
for energy_system in energy_systems:
if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
if cte.DOMESTIC_HOT_WATER in generation_system.energy_consumption:
dhw = generation_system.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
dhw = [x / 2 for x in building.domestic_hot_water_consumption[cte.HOUR]]
else:
dhw = building.domestic_hot_water_consumption[cte.HOUR]
if elec_cooling == 1:
for energy_system in energy_systems:
if cte.COOLING in energy_system.demand_types:
for generation_system in energy_system.generation_systems:
if cte.COOLING in generation_system.energy_consumption:
cooling = generation_system.energy_consumption[cte.COOLING][cte.HOUR]
else:
if len(energy_system.generation_systems) > 1:
cooling = [x / 2 for x in building.cooling_consumption[cte.HOUR]]
else:
cooling = building.cooling_consumption[cte.HOUR]
for i in range(len(building.heating_demand[cte.HOUR])):
hourly = 0
hourly += appliance[i] / 3600
hourly += lighting[i] / 3600
if heating is not None:
hourly += heating[i] / 3600
if cooling is not None:
hourly += cooling[i] / 3600
if dhw is not None:
hourly += dhw[i] / 3600
hourly_electricity_consumption.append(hourly)
return hourly_electricity_consumption
def consumption_data(city):
energy_consumption_data = {}
for building in city.buildings:
hourly_electricity_consumption = hourly_electricity_consumption_profile(building)
energy_consumption_data[f'{building.name}'] = {'heating_consumption': building.heating_consumption,
'cooling_consumption': building.cooling_consumption,
'domestic_hot_water_consumption':
building.domestic_hot_water_consumption,
'energy_consumption_breakdown':
building.energy_consumption_breakdown,
'hourly_electricity_consumption': hourly_electricity_consumption}
peak_electricity_consumption = 0
for building in energy_consumption_data:
peak_electricity_consumption += max(energy_consumption_data[building]['hourly_electricity_consumption'])
heating_demand_monthly = []
cooling_demand_monthly = []
dhw_demand_monthly = []
lighting_appliance_monthly = []
heating_consumption_monthly = []
cooling_consumption_monthly = []
dhw_consumption_monthly = []
for i in range(12):
heating_demand = 0
cooling_demand = 0
dhw_demand = 0
lighting_appliance_demand = 0
heating_consumption = 0
cooling_consumption = 0
dhw_consumption = 0
for building in city.buildings:
heating_demand += building.heating_demand[cte.MONTH][i] / 3.6e6
cooling_demand += building.cooling_demand[cte.MONTH][i] / 3.6e6
dhw_demand += building.domestic_hot_water_heat_demand[cte.MONTH][i] / 3.6e6
lighting_appliance_demand += building.lighting_electrical_demand[cte.MONTH][i] / 3.6e6
heating_consumption += building.heating_consumption[cte.MONTH][i] / 3.6e6
if building.cooling_consumption[cte.YEAR][0] == 0:
cooling_consumption += building.cooling_demand[cte.MONTH][i] / (3.6e6 * 2)
else:
cooling_consumption += building.cooling_consumption[cte.MONTH][i] / 3.6e6
dhw_consumption += building.domestic_hot_water_consumption[cte.MONTH][i] / 3.6e6
heating_demand_monthly.append(heating_demand)
cooling_demand_monthly.append(cooling_demand)
dhw_demand_monthly.append(dhw_demand)
lighting_appliance_monthly.append(lighting_appliance_demand)
heating_consumption_monthly.append(heating_consumption)
cooling_consumption_monthly.append(cooling_consumption)
dhw_consumption_monthly.append(dhw_consumption)
monthly_demands = {'heating': heating_demand_monthly,
'cooling': cooling_demand_monthly,
'dhw': dhw_demand_monthly,
'lighting_appliance': lighting_appliance_monthly}
monthly_consumptions = {'heating': heating_consumption_monthly,
'cooling': cooling_consumption_monthly,
'dhw': dhw_consumption_monthly}
yearly_heating = 0
yearly_cooling = 0
yearly_dhw = 0
yearly_appliance = 0
yearly_lighting = 0
for building in city.buildings:
yearly_appliance += building.appliances_electrical_demand[cte.YEAR][0] / 3.6e9
yearly_lighting += building.lighting_electrical_demand[cte.YEAR][0] / 3.6e9
yearly_heating += building.heating_consumption[cte.YEAR][0] / 3.6e9
yearly_cooling += building.cooling_consumption[cte.YEAR][0] / 3.6e9
yearly_dhw += building.domestic_hot_water_consumption[cte.YEAR][0] / 3.6e9
total_consumption = yearly_heating + yearly_cooling + yearly_dhw + yearly_appliance + yearly_lighting
energy_consumption_data['monthly_demands'] = monthly_demands
energy_consumption_data['monthly_consumptions'] = monthly_consumptions
energy_consumption_data['total_consumption'] = total_consumption
energy_consumption_data['maximum_hourly_electricity_consumption'] = peak_electricity_consumption
return energy_consumption_data
def cost_data(building, lcc_dataframe, cost_retrofit_scenario):
total_floor_area = 0
for thermal_zone in building.thermal_zones_from_internal_zones:
total_floor_area += thermal_zone.total_floor_area
capital_cost = lcc_dataframe.loc['total_capital_costs_systems', f'Scenario {cost_retrofit_scenario}']
operational_cost = lcc_dataframe.loc['total_operational_costs', f'Scenario {cost_retrofit_scenario}']
maintenance_cost = lcc_dataframe.loc['total_maintenance_costs', f'Scenario {cost_retrofit_scenario}']
end_of_life_cost = lcc_dataframe.loc['end_of_life_costs', f'Scenario {cost_retrofit_scenario}']
operational_income = lcc_dataframe.loc['operational_incomes', f'Scenario {cost_retrofit_scenario}']
total_life_cycle_cost = capital_cost + operational_cost + maintenance_cost + end_of_life_cost + operational_income
specific_capital_cost = capital_cost / total_floor_area
specific_operational_cost = operational_cost / total_floor_area
specific_maintenance_cost = maintenance_cost / total_floor_area
specific_end_of_life_cost = end_of_life_cost / total_floor_area
specific_operational_income = operational_income / total_floor_area
specific_life_cycle_cost = total_life_cycle_cost / total_floor_area
life_cycle_cost_analysis = {'capital_cost': capital_cost,
'capital_cost_per_sqm': specific_capital_cost,
'operational_cost': operational_cost,
'operational_cost_per_sqm': specific_operational_cost,
'maintenance_cost': maintenance_cost,
'maintenance_cost_per_sqm': specific_maintenance_cost,
'end_of_life_cost': end_of_life_cost,
'end_of_life_cost_per_sqm': specific_end_of_life_cost,
'operational_income': operational_income,
'operational_income_per_sqm': specific_operational_income,
'total_life_cycle_cost': total_life_cycle_cost,
'total_life_cycle_cost_per_sqm': specific_life_cycle_cost}
return life_cycle_cost_analysis

123
energy_system_modelling_package/random_assignation.py
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@ -1,123 +0,0 @@
"""
This project aims to assign energy systems archetype names to Montreal buildings.
The random assignation is based on statistical information extracted from different sources, being:
- For residential buildings:
- SHEU 2015: https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/sheu/2015/tables.cfm
- For non-residential buildings:
- Montreal dataportal: https://dataportalforcities.org/north-america/canada/quebec/montreal
- https://www.eia.gov/consumption/commercial/data/2018/
"""
import json
import random
from hub.city_model_structure.building import Building
energy_systems_format = 'montreal_custom'
# parameters:
residential_systems_percentage = {'system 1 gas': 15,
'system 1 electricity': 35,
'system 2 gas': 0,
'system 2 electricity': 0,
'system 3 and 4 gas': 0,
'system 3 and 4 electricity': 0,
'system 5 gas': 0,
'system 5 electricity': 0,
'system 6 gas': 0,
'system 6 electricity': 0,
'system 8 gas': 15,
'system 8 electricity': 35}
residential_new_systems_percentage = {
'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 100,
'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
'and PV': 0,
'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
'and PV': 0,
'Central Hydronic Air and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Air and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Ground and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Rooftop PV System': 0
}
non_residential_systems_percentage = {'system 1 gas': 0,
'system 1 electricity': 0,
'system 2 gas': 0,
'system 2 electricity': 0,
'system 3 and 4 gas': 39,
'system 3 and 4 electricity': 36,
'system 5 gas': 0,
'system 5 electricity': 0,
'system 6 gas': 13,
'system 6 electricity': 12,
'system 8 gas': 0,
'system 8 electricity': 0}
def _retrieve_buildings(path, year_of_construction_field=None,
function_field=None, function_to_hub=None, aliases_field=None):
_buildings = []
with open(path, 'r', encoding='utf8') as json_file:
_geojson = json.loads(json_file.read())
for feature in _geojson['features']:
_building = {}
year_of_construction = None
if year_of_construction_field is not None:
year_of_construction = int(feature['properties'][year_of_construction_field])
function = None
if function_field is not None:
function = feature['properties'][function_field]
if function_to_hub is not None:
# use the transformation dictionary to retrieve the proper function
if function in function_to_hub:
function = function_to_hub[function]
building_name = ''
building_aliases = []
if 'id' in feature:
building_name = feature['id']
if aliases_field is not None:
for alias_field in aliases_field:
building_aliases.append(feature['properties'][alias_field])
_building['year_of_construction'] = year_of_construction
_building['function'] = function
_building['building_name'] = building_name
_building['building_aliases'] = building_aliases
_buildings.append(_building)
return _buildings
def call_random(_buildings: [Building], _systems_percentage):
_buildings_with_systems = []
_systems_distribution = []
_selected_buildings = list(range(0, len(_buildings)))
random.shuffle(_selected_buildings)
total = 0
maximum = 0
add_to = 0
for _system in _systems_percentage:
if _systems_percentage[_system] > 0:
number_of_buildings = round(_systems_percentage[_system] / 100 * len(_selected_buildings))
_systems_distribution.append({'system': _system, 'number': _systems_percentage[_system],
'number_of_buildings': number_of_buildings})
if number_of_buildings > maximum:
maximum = number_of_buildings
add_to = len(_systems_distribution) - 1
total += number_of_buildings
missing = 0
if total != len(_selected_buildings):
missing = len(_selected_buildings) - total
if missing != 0:
_systems_distribution[add_to]['number_of_buildings'] += missing
_position = 0
for case in _systems_distribution:
for i in range(0, case['number_of_buildings']):
_buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
_position += 1
return _buildings

119
energy_system_modelling_package/report_creation.py
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@ -1,119 +0,0 @@
import subprocess
import datetime
import os
from pathlib import Path
class LatexReport:
def __init__(self, file_name, title, subtitle, output_path):
self.file_name = file_name
self.output_path = Path(output_path) / 'report'
self.output_path.mkdir(parents=True, exist_ok=True)
self.file_path = self.output_path / f"{file_name}.tex"
self.content = []
self.content.append(r'\documentclass{article}')
self.content.append(r'\usepackage[margin=2.5cm]{geometry}')
self.content.append(r'\usepackage{graphicx}')
self.content.append(r'\usepackage{tabularx}')
self.content.append(r'\usepackage{multirow}')
self.content.append(r'\usepackage{float}')
self.content.append(r'\begin{document}')
current_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
self.content.append(r'\title{' + title + '}')
self.content.append(r'\author{Next-Generation Cities Institute}')
self.content.append(r'\date{}')
self.content.append(r'\maketitle')
self.content.append(r'\begin{center}')
self.content.append(r'\large ' + subtitle + r'\\')
self.content.append(r'\large ' + current_datetime)
self.content.append(r'\end{center}')
def add_section(self, section_title):
self.content.append(r'\section{' + section_title + r'}')
def add_subsection(self, subsection_title):
self.content.append(r'\subsection{' + subsection_title + r'}')
def add_subsubsection(self, subsection_title):
self.content.append(r'\subsubsection{' + subsection_title + r'}')
def add_text(self, text):
self.content.append(text)
def add_table(self, table_data, caption=None, first_column_width=None, merge_first_column=False):
num_columns = len(table_data[0])
total_width = 0.9
first_column_width_str = ''
if first_column_width is not None:
first_column_width_str = str(first_column_width) + 'cm'
total_width -= first_column_width / 16.0
if caption:
self.content.append(r'\begin{table}[htbp]')
self.content.append(r'\caption{' + caption + r'}')
self.content.append(r'\centering')
column_format = '|p{' + first_column_width_str + r'}|' + '|'.join(
['X'] * (num_columns - 1)) + '|' if first_column_width is not None else '|' + '|'.join(['X'] * num_columns) + '|'
self.content.append(r'\begin{tabularx}{\textwidth}{' + column_format + '}')
self.content.append(r'\hline')
previous_first_column = None
rowspan_count = 1
for i, row in enumerate(table_data):
if merge_first_column and i > 0 and row[0] == previous_first_column:
rowspan_count += 1
self.content.append(' & '.join(['' if j == 0 else cell for j, cell in enumerate(row)]) + r' \\')
else:
if merge_first_column and i > 0 and rowspan_count > 1:
self.content[-rowspan_count] = self.content[-rowspan_count].replace(r'\multirow{1}',
r'\multirow{' + str(rowspan_count) + '}')
rowspan_count = 1
if merge_first_column and i < len(table_data) - 1 and row[0] == table_data[i + 1][0]:
self.content.append(r'\multirow{1}{*}{' + row[0] + '}' + ' & ' + ' & '.join(row[1:]) + r' \\')
else:
self.content.append(' & '.join(row) + r' \\')
previous_first_column = row[0]
self.content.append(r'\hline')
if merge_first_column and rowspan_count > 1:
self.content[-rowspan_count] = self.content[-rowspan_count].replace(r'\multirow{1}',
r'\multirow{' + str(rowspan_count) + '}')
self.content.append(r'\end{tabularx}')
if caption:
self.content.append(r'\end{table}')
def add_image(self, image_path, caption=None, placement='ht'):
if caption:
self.content.append(r'\begin{figure}[' + placement + r']')
self.content.append(r'\centering')
self.content.append(r'\includegraphics[width=\textwidth]{' + image_path + r'}')
self.content.append(r'\caption{' + caption + r'}')
self.content.append(r'\end{figure}')
else:
self.content.append(r'\begin{figure}[' + placement + r']')
self.content.append(r'\centering')
self.content.append(r'\includegraphics[width=\textwidth]{' + image_path + r'}')
self.content.append(r'\end{figure}')
def add_itemize(self, items):
self.content.append(r'\begin{itemize}')
for item in items:
self.content.append(r'\item ' + item)
self.content.append(r'\end{itemize}')
def save_report(self):
self.content.append(r'\end{document}')
with open(self.file_path, 'w') as f:
f.write('\n'.join(self.content))
def compile_to_pdf(self):
subprocess.run(['pdflatex', '-output-directory', str(self.output_path), str(self.file_path)])

16
input_files/omhm_selected_buildings.geojson Normal file
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@ -0,0 +1,16 @@
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
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}

0
base_case_modelling.py → scripts/base_case_modelling.py
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0
monthly_dhw.py → scripts/monthly_dhw.py
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0
monthly_hvac_plots.py → scripts/monthly_hvac_plots.py
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0
peak_load.py → scripts/peak_load.py
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