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Ongoing developments for the course workshop, including graphs implementation

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Oriol Gavalda 2023-07-10 17:54:05 -04:00
parent c9d345e1cb
commit 81ac7bc0c3
4 changed files with 177 additions and 50 deletions
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95
costs/Individualplot.py Normal file
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@ -0,0 +1,95 @@
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import plotly.express as px
def individualplot(output_yearly_graph,retrofitting_scenario) :
# Sample data
categories = output_yearly_graph.index
bar_data_1 = output_yearly_graph['Capital']
bar_data_2 = -output_yearly_graph['Capital incomes']
bar_data_3 = output_yearly_graph['End of life']
bar_data_4 = output_yearly_graph['Operational total']
bar_data_5 = -output_yearly_graph['Operational income']
line_data = output_yearly_graph['Common addition']
# Create bar trace 1
bar_trace_1 = go.Bar(
x=categories,
y=bar_data_1,
name='Capital',
yaxis='y1'
)
# Create bar trace 2
bar_trace_2 = go.Bar(
x=categories,
y=bar_data_2,
name='Capital incomes',
yaxis='y1',
marker=dict(color='red')
)
# Create bar trace 2
bar_trace_3 = go.Bar(
x=categories,
y=bar_data_3,
name='End of life',
yaxis='y1'
)
# Create bar trace 2
bar_trace_4 = go.Bar(
x=categories,
y=bar_data_4,
name='Operational total',
yaxis='y1'
)
# Create bar trace 2
bar_trace_5 = go.Bar(
x=categories,
y=bar_data_5,
name='Operational income',
yaxis='y1',
marker=dict(color='red')
)
# Create line trace
line_trace = go.Scatter(
x=categories,
y=line_data,
mode='lines',
name='Common addition',
yaxis='y2'
)
# Create layout
layout = go.Layout(
title='Stacked Bar Chart with Negative Values and Line',
xaxis=dict(title='Categories'),
yaxis=dict(title='Bar Values', side='left', showgrid=False),
yaxis2=dict(title='Line Values', side='right', overlaying='y', showgrid=False),
barmode='stack'
)
# Create figure
fig = go.Figure(data=[bar_trace_1, bar_trace_2, bar_trace_3, bar_trace_4, bar_trace_5, line_trace], layout=layout)
fig.show()
fig, ax1 = plt.subplots()
bottom = [0]*len(bar_data_1)
ax1.bar(categories, bar_data_1, label='Capital costs', color='green')
ax1.bar(categories, bar_data_3, label='End of life costs', color='grey')
ax1.bar(categories, bar_data_4, label='Operational costs', color='brown')
ax1.bar(categories, bar_data_2, label='Capital incomes', color='red', bottom=bottom)
ax1.bar(categories, bar_data_5, label='Operational incomes', color='orange', bottom=bar_data_2)
ax1.set_ylabel('Values')
ax1.set_title('Stacked Bar Chart with Line')
ax1.legend()
# Create the line chart on a secondary y-axis
ax2 = ax1.twinx()
ax2.plot(categories, line_data, marker='o', linestyle='-', color='blue', label='Line')
ax2.set_ylabel('Line Values')
ax2.legend()
plt.show()

110
costs/__main__.py
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@ -19,6 +19,7 @@ from hub.imports.weather_factory import WeatherFactory
from monthly_energy_balance_engine import MonthlyEnergyBalanceEngine
from sra_engine import SraEngine
from printing_results import *
from Individualplot import *
from hub.helpers import constants as cte
from life_cycle_costs import LifeCycleCosts
@ -59,45 +60,56 @@ out_path = (Path(__file__).parent.parent / 'out_files')
tmp_folder = (Path(__file__).parent / 'tmp')
print('[simulation start]')
city = GeometryFactory('geojson',
path=file_path,
height_field='citygml_me',
year_of_construction_field='ANNEE_CONS',
function_field='CODE_UTILI',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
city.climate_reference_city = climate_reference_city
city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
city_original = GeometryFactory('geojson',
path=file_path,
height_field='citygml_me',
year_of_construction_field='ANNEE_CONS',
function_field='CODE_UTILI',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
city_original.climate_reference_city = climate_reference_city
city_original.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
print(f'city created from {file_path}')
WeatherFactory(weather_format, city).enrich()
WeatherFactory(weather_format, city_original).enrich()
print('enrich weather... done')
ConstructionFactory(construction_format, city).enrich()
ConstructionFactory(construction_format, city_original).enrich()
print('enrich constructions... done')
UsageFactory(usage_format, city).enrich()
UsageFactory(usage_format, city_original).enrich()
print('enrich usage... done')
for building in city.buildings:
building.energy_systems_archetype_name = 'system 1 gas pv'
EnergySystemsFactory(energy_systems_format, city).enrich()
for building in city_original.buildings:
building.energy_systems_archetype_name = 'system 1 gas'
EnergySystemsFactory(energy_systems_format, city_original).enrich()
print('enrich systems... done')
print('exporting:')
sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
SraEngine(city, sra_file, tmp_folder)
sra_file = (tmp_folder / f'{city_original.name}_sra.xml').resolve()
SraEngine(city_original, sra_file, tmp_folder)
print(' sra processed...')
catalog = CostCatalogFactory('montreal_custom').catalog
investmentcosts = pd.DataFrame([])
print(f'retrofitting 0')
for retrofitting_scenario in RETROFITTING_SCENARIOS:
city = city_original.copy
total_floor_area = 0
if retrofitting_scenario in (SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV):
if retrofitting_scenario == SYSTEM_RETROFIT_AND_PV:
print(f'retrofitting 1')
for building in city.buildings:
building.energy_systems_archetype_name = 'system 7 electricity pv'
EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
if retrofitting_scenario == SKIN_RETROFIT:
for building in city.buildings:
building.year_of_construction = RETROFITTING_YEAR_CONSTRUCTION
building.energy_systems_archetype_name = 'system 1 gas'
ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
print('enrich retrofitted constructions... done')
print(f'retrofitting 2')
if retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
if retrofitting_scenario == SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV:
for building in city.buildings:
building.energy_systems_archetype_name = 'system 6 electricity pv'
building.year_of_construction = RETROFITTING_YEAR_CONSTRUCTION
building.energy_systems_archetype_name = 'system 7 electricity pv'
ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
print('enrich systems... done')
print(f'retrofitting 3')
MonthlyEnergyBalanceEngine(city, tmp_folder)
print(' insel processed...')
@ -108,34 +120,54 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
energy_system.generation_system.heat_power = building.heating_peak_load[cte.YEAR][0]
if cte.COOLING in energy_system.demand_types:
energy_system.generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][0]
print(f' heating consumption {building.heating_consumption[cte.YEAR][0]}')
print('importing results:')
results = Results(city, out_path)
results.print()
print('results printed...')
print('[simulation end]')
print(f'beginning costing scenario {retrofitting_scenario} systems... done')
for building in city.buildings:
total_floor_area = 0
function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
archetype = _search_archetype(catalog, function)
print('lcc for first building started')
print(building.heating_consumption[cte.YEAR][0])
if "gas" in building.energy_systems_archetype_name:
FUEL_TYPE = 1
else:
FUEL_TYPE = 0
lcc = LifeCycleCosts(building, archetype, NUMBER_OF_YEARS, CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX,
ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE, retrofitting_scenario, FUEL_TYPE)
global_capital_costs, global_capital_incomes = lcc.calculate_capital_costs()
global_end_of_life_costs = lcc.calculate_end_of_life_costs()
global_operational_costs = lcc.calculate_total_operational_costs
global_operational_costs = lcc.calculate_total_operational_costs()
global_maintenance_costs = lcc.calculate_total_maintenance_costs()
global_operational_incomes = lcc.calculate_total_operational_incomes(retrofitting_scenario)
global_operational_incomes = lcc.calculate_total_operational_incomes()
total_plot_costs = global_capital_costs - global_capital_incomes + global_end_of_life_costs + \
global_operational_costs + global_maintenance_costs - global_operational_incomes
capital_total = global_capital_costs.sum(axis=1)
capital_income_total = global_capital_incomes.sum(axis=1)
end_of_life_total = global_end_of_life_costs.sum(axis=1)
operational_total = global_operational_costs.sum(axis=1)
maintenance_total = global_maintenance_costs.sum(axis=1)
operational_income_total = global_operational_incomes.sum(axis=1)
lineatotal = capital_total - capital_income_total + end_of_life_total + operational_total + maintenance_total - \
operational_income_total
lineatotal = lineatotal.cumsum()
print(lineatotal)
output_yearly_graph = pd.DataFrame({'Capital': capital_total,
'Capital incomes' : capital_income_total,
'End of life' : end_of_life_total,
'Operational total': operational_total,
'Maintenance total': maintenance_total,
'Operational income': operational_income_total,'Common addition': lineatotal})
individualplot(output_yearly_graph, retrofitting_scenario)
full_path_output = Path(out_path / f'output {retrofitting_scenario} {building.name}.xlsx').resolve()
with pd.ExcelWriter(full_path_output) as writer:
global_capital_costs.to_excel(writer, sheet_name='global_capital_costs')
global_end_of_life_costs.to_excel(writer, sheet_name='global_end_of_life_costs')
@ -144,7 +176,6 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
global_operational_incomes.to_excel(writer, sheet_name='global_operational_incomes')
global_capital_incomes.to_excel(writer, sheet_name='global_capital_incomes')
investmentcosts = pd.DataFrame([])
print('RETROFITTING SCENARIO', retrofitting_scenario)
if retrofitting_scenario == 0:
investmentcosts = [global_capital_costs['B2010_opaque_walls'][0],
@ -171,6 +202,8 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
investmentcosts.index = ['Opaque walls', 'Transparent walls', 'Opaque roof', 'Superstructure',
'Heat generation systems', 'Other HVAC AHU', 'Lighting and branch wiring', 'PV systems']
investmentcosts = investmentcosts.applymap(lambda x: round(x, 2))
df_capital_costs_skin = (
global_capital_costs['B2010_opaque_walls'] + global_capital_costs['B2020_transparent'] +
global_capital_costs['B3010_opaque_roof'] + global_capital_costs['B10_superstructure']
@ -239,9 +272,10 @@ for retrofitting_scenario in RETROFITTING_SCENARIOS:
'operational_incomes',
'capital_incomes']
print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')
life_cycle_results = life_cycle_results.applymap(lambda x: round(x, 2))
life_cycle_costs = round(life_cycle_costs, 2)
# printing_results(investmentcosts, life_cycle_results, total_floor_area)
printing_results(investmentcosts, life_cycle_results, total_floor_area)

18
costs/life_cycle_costs.py
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@ -249,7 +249,7 @@ class LifeCycleCosts:
def calculate_total_floor_area(self):
total_floor_area = self._total_floor_area
return total_floor_area
@property
def calculate_total_operational_costs(self):
"""
Calculate total operational costs
@ -270,8 +270,9 @@ class LifeCycleCosts:
(building.heating_consumption[cte.YEAR][0] + building.domestic_hot_water_consumption[cte.YEAR][0]) / 1000 *
archetype.operational_cost.fuels[1].variable[0]
)
if self._fuel_type == 0:
electricity_heating = building.heating_consumption[cte.YEAR][0] / 1000
else:
# todo: change hardcoded 3 to include COP heating system
electricity_heating = building.heating_consumption[cte.YEAR][0] /(1000)
domestic_hot_water_electricity = building.domestic_hot_water_consumption[cte.YEAR][0] / 1000
electricity_cooling = building.cooling_consumption[cte.YEAR][0] / 1000
@ -282,14 +283,12 @@ class LifeCycleCosts:
electricity_heating + electricity_cooling + electricity_lighting + domestic_hot_water_electricity +
electricity_plug_loads + electricity_distribution
)
print(f'electricity consumption {total_electricity_consumption}')
# todo: change when peak electricity demand is coded. Careful with factor residential
peak_electricity_demand = 0.1*total_floor_area # self._peak_electricity_demand
variable_electricity_cost_year_0 = total_electricity_consumption * archetype.operational_cost.fuels[0].variable[0]
peak_electricity_cost_year_0 = peak_electricity_demand * archetype.operational_cost.fuels[0].fixed_power * 12
monthly_electricity_cost_year_0 = archetype.operational_cost.fuels[0].fixed_monthly * 12 * factor_residential
variable_electricity_cost_year_0 = total_electricity_consumption * archetype.operational_cost.fuels[0].variable[0]
for year in range(1, self._number_of_years + 1):
price_increase_electricity = math.pow(1 + self._electricity_price_index, year)
price_increase_peak_electricity = math.pow(1 + self._electricity_peak_index, year)
@ -315,7 +314,7 @@ class LifeCycleCosts:
return self._yearly_operational_costs
def calculate_total_operational_incomes(self, retrofitting_scenario):
def calculate_total_operational_incomes(self):
"""
Calculate total operational incomes
:return: pd.DataFrame
@ -324,10 +323,7 @@ class LifeCycleCosts:
if cte.YEAR not in building.onsite_electrical_production:
onsite_electricity_production = 0
else:
if retrofitting_scenario == 0 or retrofitting_scenario == 1:
onsite_electricity_production = 0
else:
onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0]/1000
onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0]/1000
for year in range(1, self._number_of_years + 1):
price_increase_electricity = math.pow(1 + self._electricity_price_index, year)

4
costs/printing_results.py
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@ -55,4 +55,6 @@ def printing_results(investmentcosts, life_cycle_results,total_floor_area):
fig = px.bar(df_swapped, title='Life Cycle Costs for buildings')
fig.show()
# Display the chart
plt.show()
plt.show()