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Pilar Monsalvete 2023-08-07 12:32:33 -04:00
parent e138a24555
commit 67cd435fd5
41 changed files with 252 additions and 1533 deletions
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51
hub/city_model_structure/building.py
View File

@ -296,7 +296,7 @@ class Building(CityObject):
def cold_water_temperature(self) -> {float}:
"""
Get cold water temperature in degrees Celsius
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
return self._cold_water_temperature
@ -304,7 +304,7 @@ class Building(CityObject):
def cold_water_temperature(self, value):
"""
Set cold water temperature in degrees Celsius
:param value: dict{DataFrame(float)}
:param value: dict{[float]}
"""
self._cold_water_temperature = value
@ -312,7 +312,7 @@ class Building(CityObject):
def heating_demand(self) -> dict:
"""
Get heating demand in Wh
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
return self._heating_demand
@ -320,7 +320,7 @@ class Building(CityObject):
def heating_demand(self, value):
"""
Set heating demand in Wh
:param value: dict{DataFrame(float)}
:param value: dict{[float]}
"""
self._heating_demand = value
@ -328,7 +328,7 @@ class Building(CityObject):
def cooling_demand(self) -> dict:
"""
Get cooling demand in Wh
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
return self._cooling_demand
@ -336,7 +336,7 @@ class Building(CityObject):
def cooling_demand(self, value):
"""
Set cooling demand in Wh
:param value: dict{DataFrame(float)}
:param value: dict{[float]}
"""
self._cooling_demand = value
@ -344,7 +344,7 @@ class Building(CityObject):
def lighting_electrical_demand(self) -> dict:
"""
Get lighting electrical demand in Wh
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
return self._lighting_electrical_demand
@ -352,7 +352,7 @@ class Building(CityObject):
def lighting_electrical_demand(self, value):
"""
Set lighting electrical demand in Wh
:param value: dict{DataFrame(float)}
:param value: dict{[float]}
"""
self._lighting_electrical_demand = value
@ -360,7 +360,7 @@ class Building(CityObject):
def appliances_electrical_demand(self) -> dict:
"""
Get appliances electrical demand in Wh
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
return self._appliances_electrical_demand
@ -368,7 +368,7 @@ class Building(CityObject):
def appliances_electrical_demand(self, value):
"""
Set appliances electrical demand in Wh
:param value: dict{DataFrame(float)}
:param value: dict{[float]}
"""
self._appliances_electrical_demand = value
@ -376,7 +376,7 @@ class Building(CityObject):
def domestic_hot_water_heat_demand(self) -> dict:
"""
Get domestic hot water heat demand in Wh
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
return self._domestic_hot_water_heat_demand
@ -384,7 +384,7 @@ class Building(CityObject):
def domestic_hot_water_heat_demand(self, value):
"""
Set domestic hot water heat demand in Wh
:param value: dict{DataFrame(float)}
:param value: dict{[float]}
"""
self._domestic_hot_water_heat_demand = value
@ -428,12 +428,12 @@ class Building(CityObject):
def heating_peak_load(self) -> Union[None, dict]:
"""
Get heating peak load in W
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
results = {}
if cte.HOUR in self.heating_demand:
monthly_values = PeakLoads().\
peak_loads_from_hourly(self.heating_demand[cte.HOUR][next(iter(self.heating_demand[cte.HOUR]))])
peak_loads_from_hourly(self.heating_demand[cte.HOUR])
else:
monthly_values = PeakLoads(self).heating_peak_loads_from_methodology
if monthly_values is None:
@ -446,11 +446,11 @@ class Building(CityObject):
def cooling_peak_load(self) -> Union[None, dict]:
"""
Get cooling peak load in W
:return: dict{DataFrame(float)}
:return: dict{[float]}
"""
results = {}
if cte.HOUR in self.cooling_demand:
monthly_values = PeakLoads().peak_loads_from_hourly(self.cooling_demand[cte.HOUR][next(iter(self.cooling_demand[cte.HOUR]))])
monthly_values = PeakLoads().peak_loads_from_hourly(self.cooling_demand[cte.HOUR])
else:
monthly_values = PeakLoads(self).cooling_peak_loads_from_methodology
if monthly_values is None:
@ -614,7 +614,8 @@ class Building(CityObject):
"""
if len(self._heating_consumption) == 0:
for heating_demand_key in self.heating_demand:
demand = self.heating_demand[heating_demand_key][cte.INSEL_MEB]
demand = self.heating_demand[heating_demand_key]
print('AAAAAAAAAA', heating_demand_key, demand)
consumption_type = cte.HEATING
final_energy_consumed = self._calculate_consumption(consumption_type, demand)
if final_energy_consumed is None:
@ -630,7 +631,7 @@ class Building(CityObject):
"""
if len(self._cooling_consumption) == 0:
for cooling_demand_key in self.cooling_demand:
demand = self.cooling_demand[cooling_demand_key][cte.INSEL_MEB]
demand = self.cooling_demand[cooling_demand_key]
consumption_type = cte.COOLING
final_energy_consumed = self._calculate_consumption(consumption_type, demand)
if final_energy_consumed is None:
@ -646,7 +647,7 @@ class Building(CityObject):
"""
if len(self._domestic_hot_water_consumption) == 0:
for domestic_hot_water_demand_key in self.domestic_hot_water_heat_demand:
demand = self.domestic_hot_water_heat_demand[domestic_hot_water_demand_key][cte.INSEL_MEB]
demand = self.domestic_hot_water_heat_demand[domestic_hot_water_demand_key]
consumption_type = cte.DOMESTIC_HOT_WATER
final_energy_consumed = self._calculate_consumption(consumption_type, demand)
if final_energy_consumed is None:
@ -677,7 +678,7 @@ class Building(CityObject):
_total_hours = 0
for key in _working_hours:
hours = sum(_working_hours[key])
_total_hours += hours * cte.DAYS_A_YEAR[key]
_total_hours += hours * cte.WEEK_DAYS_A_YEAR[key]
return _total_hours
@property
@ -710,8 +711,8 @@ class Building(CityObject):
if _peak_load_type == cte.HEATING.lower():
_consumption_fix_flow = distribution_system.distribution_consumption_fix_flow
for heating_demand_key in self.heating_demand:
_consumption = [0]*len(self.heating_demand[heating_demand_key][cte.INSEL_MEB])
_demand = self.heating_demand[heating_demand_key][cte.INSEL_MEB]
_consumption = [0]*len(self.heating_demand[heating_demand_key])
_demand = self.heating_demand[heating_demand_key]
for i, _ in enumerate(_consumption):
_consumption[i] += (parasitic_energy_consumption + consumption_variable_flow) * _demand[i]
self._distribution_systems_electrical_consumption[heating_demand_key] = _consumption
@ -720,7 +721,7 @@ class Building(CityObject):
_consumption_fix_flow = distribution_system.distribution_consumption_fix_flow
for demand_key in self.cooling_demand:
_consumption = self._distribution_systems_electrical_consumption[demand_key]
_demand = self.cooling_demand[demand_key][cte.INSEL_MEB]
_demand = self.cooling_demand[demand_key]
for i, _ in enumerate(_consumption):
_consumption[i] += (parasitic_energy_consumption + consumption_variable_flow) * _demand[i]
self._distribution_systems_electrical_consumption[demand_key] = _consumption
@ -780,12 +781,12 @@ class Building(CityObject):
_efficiency = energy_system.generation_system.generic_generation_system.electricity_efficiency
self._onsite_electrical_production = {}
for _key in self.roofs[0].global_irradiance.keys():
_results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key][cte.SRA]))]
_results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key]))]
for surface in self.roofs:
if _key in orientation_losses_factor:
_results = [x + y * _efficiency * surface.perimeter_area
* surface.solar_collectors_area_reduction_factor * z
for x, y, z in zip(_results, surface.global_irradiance[_key][cte.SRA],
for x, y, z in zip(_results, surface.global_irradiance[_key],
orientation_losses_factor[_key]['south'])]
self._onsite_electrical_production[_key] = _results
return self._onsite_electrical_production

4
hub/city_model_structure/building_demand/surface.py
View File

@ -179,7 +179,7 @@ class Surface:
def global_irradiance(self) -> dict:
"""
Get global irradiance on surface in Wh/m2
:return: dict{DataFrame(float)}
:return: dict
"""
return self._global_irradiance
@ -187,7 +187,7 @@ class Surface:
def global_irradiance(self, value):
"""
Set global irradiance on surface in Wh/m2
:param value: dict{DataFrame(float)}
:param value: dict
"""
self._global_irradiance = value

16
hub/city_model_structure/city.py
View File

@ -25,7 +25,6 @@ from hub.city_model_structure.building import Building
from hub.city_model_structure.buildings_cluster import BuildingsCluster
from hub.city_model_structure.city_object import CityObject
from hub.city_model_structure.city_objects_cluster import CityObjectsCluster
from hub.city_model_structure.energy_system import EnergySystem
from hub.city_model_structure.iot.station import Station
from hub.city_model_structure.level_of_detail import LevelOfDetail
from hub.city_model_structure.parts_consisting_building import PartsConsistingBuilding
@ -163,9 +162,6 @@ class City:
if self.buildings is not None:
for building in self.buildings:
self._city_objects.append(building)
if self.energy_systems is not None:
for energy_system in self.energy_systems:
self._city_objects.append(energy_system)
return self._city_objects
@property
@ -409,14 +405,6 @@ class City:
"""
return self._parts_consisting_buildings
@property
def energy_systems(self) -> Union[List[EnergySystem], None]:
"""
Get energy systems belonging to the city
:return: None or [EnergySystem]
"""
return self._energy_systems
@property
def stations(self) -> [Station]:
"""
@ -478,12 +466,12 @@ class City:
parameter_city_building_total_radiation = 0
for surface in building.surfaces:
if surface.global_irradiance:
parameter_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
parameter_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
merged_city_building_total_radiation = 0
for surface in merged_city.city_object(building.name).surfaces:
if surface.global_irradiance:
merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
if merged_city_building_total_radiation == 0:
merged_city.remove_city_object(merged_city.city_object(building.name))

17
hub/city_model_structure/city_object.py
View File

@ -172,7 +172,7 @@ class CityObject:
def external_temperature(self) -> {float}:
"""
Get external temperature surrounding the city object in Celsius
:return: dict{DataFrame(float)}
:return: dict{dict{[float]}}
"""
return self._external_temperature
@ -180,11 +180,10 @@ class CityObject:
def external_temperature(self, value):
"""
Set external temperature surrounding the city object in Celsius
:param value: dict{DataFrame(float)}
:param value: dict{dict{[float]}}
"""
self._external_temperature = value
# todo: this is the new format we will use to get rid of the data frames
@property
def ground_temperature(self) -> dict:
"""
@ -206,7 +205,7 @@ class CityObject:
def global_horizontal(self) -> dict:
"""
Get global horizontal radiation surrounding the city object in W/m2
:return: dict{DataFrame(float)}
:return: dict{dict{[float]}}
"""
return self._global_horizontal
@ -214,7 +213,7 @@ class CityObject:
def global_horizontal(self, value):
"""
Set global horizontal radiation surrounding the city object in W/m2
:param value: dict{DataFrame(float)}
:param value: dict{dict{[float]}}
"""
self._global_horizontal = value
@ -222,7 +221,7 @@ class CityObject:
def diffuse(self) -> dict:
"""
Get diffuse radiation surrounding the city object in W/m2
:return: dict{DataFrame(float)}
:return: dict{dict{[float]}}
"""
return self._diffuse
@ -230,7 +229,7 @@ class CityObject:
def diffuse(self, value):
"""
Set diffuse radiation surrounding the city object in W/m2
:param value: dict{DataFrame(float)}
:param value: dict{dict{[float]}}
"""
self._diffuse = value
@ -238,7 +237,7 @@ class CityObject:
def beam(self) -> dict:
"""
Get beam radiation surrounding the city object in W/m2
:return: dict{DataFrame(float)}
:return: dict{dict{[float]}}
"""
return self._beam
@ -246,7 +245,7 @@ class CityObject:
def beam(self, value):
"""
Set beam radiation surrounding the city object in W/m2
:param value: dict{DataFrame(float)}
:param value: dict{dict{[float]}}
"""
self._beam = value

65
hub/city_model_structure/energy_system.py
View File

@ -1,65 +0,0 @@
"""
EnergySystem module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributors: Peter Yefi peteryefi@gmail.com
"""
from hub.city_model_structure.city_object import CityObject
from hub.city_model_structure.energy_systems.air_source_hp import AirSourceHP
from hub.city_model_structure.energy_systems.water_to_water_hp import WaterToWaterHP
class EnergySystem(CityObject):
"""
EnergySystem(CityObject) class
"""
def __init__(self, name, surfaces):
super().__init__(name, surfaces)
self._air_source_hp = None
self._water_to_water_hp = None
self._type = 'energy_system'
@property
def air_source_hp(self) -> AirSourceHP:
"""
Heat pump energy system
:return:
"""
return self._air_source_hp
@air_source_hp.setter
def air_source_hp(self, value):
"""
Set heat pump for energy system
:param value: AirSourceHP
"""
if self._air_source_hp is None:
self._air_source_hp = value
@property
def water_to_water_hp(self) -> WaterToWaterHP:
"""
Water to water heat pump energy system
:return:
"""
return self._water_to_water_hp
@water_to_water_hp.setter
def water_to_water_hp(self, value):
"""
Set water to water heat pump for energy system
:param value: WaterToWaterHP
"""
if self._water_to_water_hp is None:
self._water_to_water_hp = value
@property
def type(self) -> str:
"""
Type of city object
:return: str
"""
return self._type

132
hub/city_model_structure/energy_systems/air_source_hp.py
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@ -1,132 +0,0 @@
"""
air_source_hp module defines an air source heat pump
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
Code contributors: Peter Yefi peteryefi@gmail.com
"""
from typing import List
from hub.city_model_structure.energy_systems.heat_pump import HeatPump
class AirSourceHP(HeatPump):
"""
AirSourceHP class
"""
def __init__(self):
super().__init__()
self._cooling_capacity = None
self._cooling_comp_power = None
self._cooling_capacity_coff = None # a coefficients for insel
self._heating_capacity = None
self._heating_comp_power = None
self._heating_capacity_coff = None
@property
def cooling_capacity(self) -> List[float]:
"""
Get cooling capacity in kW
:return: [[float]]
"""
return self._cooling_capacity
@cooling_capacity.setter
def cooling_capacity(self, value):
"""
Set cooling capacity in kW
:param value: [[float]]
"""
if self._cooling_capacity is None:
self._cooling_capacity = value
@property
def cooling_comp_power(self) -> List[float]:
"""
Get cooling compressor power input in kW
:return: [[float]]
"""
return self._cooling_comp_power
@cooling_comp_power.setter
def cooling_comp_power(self, value):
"""
Set the cooling compressor in kW
:param value: [[float]]
:return:
"""
if self._cooling_comp_power is None:
self._cooling_comp_power = value
@property
def cooling_capacity_coff(self) -> List[float]:
"""
Get cooling capacity coefficients
:return: [float]
"""
return self._cooling_capacity_coff
@cooling_capacity_coff.setter
def cooling_capacity_coff(self, value):
"""
Set the value for cooling capacity coefficients
:param value: [float]
:return:
"""
if self._cooling_capacity_coff is None:
self._cooling_capacity_coff = value
@property
def heating_capacity(self) -> List[float]:
"""
Get heating capacity kW
:return: [[float]]
"""
return self._heating_capacity
@heating_capacity.setter
def heating_capacity(self, value):
"""
Set the heating capacity in kW
:param value: [[float]]
:return:
"""
if self._heating_capacity is None:
self._heating_capacity = value
@property
def heating_comp_power(self) -> List[float]:
"""
Get heating compressor power kW
:return: [[float]]
"""
return self._heating_comp_power
@heating_comp_power.setter
def heating_comp_power(self, value):
"""
Set the heating compressor power in kW
:param value: [[float]]
:return:
"""
if self._heating_comp_power is None:
self._heating_comp_power = value
@property
def heating_capacity_coff(self) -> List[float]:
"""
Get heating capacity coefficients
:return: [float]
"""
return self._heating_capacity_coff
@heating_capacity_coff.setter
def heating_capacity_coff(self, value):
"""
Set the value for heating capacity coefficients
:param value: [float]
:return:
"""
if self._heating_capacity_coff is None:
self._heating_capacity_coff = value

131
hub/city_model_structure/energy_systems/water_to_water_hp.py
View File

@ -1,131 +0,0 @@
"""
water_to_water_hp module defines a water to water heat pump heat pump
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Peter Yefi peteryefi@gmail.com
"""
from typing import List
from hub.city_model_structure.energy_systems.heat_pump import HeatPump
class WaterToWaterHP(HeatPump):
"""
WaterToWaterHP class
"""
def __init__(self):
super().__init__()
self._entering_water_temp = None
self._leaving_water_temp = None
self._total_cooling_capacity = None
self._power_demand = None
self._flow_rate = None
self._power_demand_coff = None # a coefficients
@property
def entering_water_temp(self) -> List[float]:
"""
Get entering water temperature in degree celsius
:return: [[float]]
"""
return self._entering_water_temp
@entering_water_temp.setter
def entering_water_temp(self, value):
"""
Set entering water temperature in degree celsius
:param value: [[float]]
"""
if self._entering_water_temp is None:
self._entering_water_temp = value
@property
def leaving_water_temp(self) -> List[float]:
"""
Get leaving water temperature in degree celsius
:return: [[float]]
"""
return self._leaving_water_temp
@leaving_water_temp.setter
def leaving_water_temp(self, value):
"""
Set the leaving water temperature in degree celsius
:param value: [[float]]
:return:
"""
if self._leaving_water_temp is None:
self._leaving_water_temp = value
@property
def total_cooling_capacity(self) -> List[float]:
"""
Get total cooling capacity
:return: [float]
"""
return self._total_cooling_capacity
@total_cooling_capacity.setter
def total_cooling_capacity(self, value):
"""
Set the value for total cooling capacity
:param value: [float]
:return:
"""
if self._total_cooling_capacity is None:
self._total_cooling_capacity = value
@property
def power_demand(self) -> List[float]:
"""
Get power demand in kW
:return: [float]
"""
return self._power_demand
@power_demand.setter
def power_demand(self, value):
"""
Set the value for power demand in kW
:param value: [float]
:return:
"""
if self._power_demand is None:
self._power_demand = value
@property
def flow_rate(self) -> List[float]:
"""
Get flow rate in kg/s
:return: [[float]]
"""
return self._flow_rate
@flow_rate.setter
def flow_rate(self, value):
"""
Set flow rate in kW
:param value: [[float]]
:return:
"""
if self._flow_rate is None:
self._flow_rate = value
@property
def power_demand_coff(self) -> List[float]:
"""
Get power demand coefficients
:return: [float]
"""
return self._power_demand_coff
@power_demand_coff.setter
def power_demand_coff(self, value):
"""
Set the value for power demand coefficients
:param value: [float]
:return:
"""
if self._power_demand_coff is None:
self._power_demand_coff = value

2
hub/exports/building_energy/idf.py
View File

@ -533,7 +533,7 @@ class Idf:
self._add_schedules(usage, 'Appliance', thermal_zone.appliances.schedules)
self._add_schedules(usage, 'DHW_prof', thermal_zone.domestic_hot_water.schedules)
_new_schedules = self._create_yearly_values_schedules('cold_temp',
building.cold_water_temperature[cte.HOUR]['epw'])
building.cold_water_temperature[cte.HOUR])
self._add_schedules(building.name, 'cold_temp', _new_schedules)
value = thermal_zone.domestic_hot_water.service_temperature
_new_schedules = self._create_constant_value_schedules('DHW_temp', value)

24
hub/exports/building_energy/insel/insel_monthly_energy_balance.py
View File

@ -30,13 +30,11 @@ class InselMonthlyEnergyBalance:
"""
Insel monthly energy balance class
"""
def __init__(self, city, path, custom_insel_block, radiation_calculation_method='sra', weather_format='epw'):
def __init__(self, city, path, custom_insel_block):
self._city = city
self._path = path
self._custom_insel_block = custom_insel_block
self._results = None
self._radiation_calculation_method = radiation_calculation_method
self._weather_format = weather_format
self._contents = []
self._insel_files_paths = []
self._sanity_check()
@ -48,7 +46,7 @@ class InselMonthlyEnergyBalance:
logging.warning('Building %s has missing values. Monthly Energy Balance cannot be processed', building.name)
self._contents.append(
self._generate_meb_template(building, output_path, self._radiation_calculation_method, self._weather_format, self._custom_insel_block)
self._generate_meb_template(building, output_path, self._custom_insel_block)
)
self._export()
@ -96,7 +94,7 @@ class InselMonthlyEnergyBalance:
f'Required minimum level 1')
@staticmethod
def _generate_meb_template(building, insel_outputs_path, radiation_calculation_method, weather_format, custom_insel_block):
def _generate_meb_template(building, insel_outputs_path, custom_insel_block):
file = ""
i_block = 1
parameters = ["1", "12", "1"]
@ -148,7 +146,7 @@ class InselMonthlyEnergyBalance:
total_values = sum(schedule.values)
total_hours = 0
for day_type in schedule.day_types:
total_hours += cte.DAYS_A_YEAR[day_type] / 365 / 24
total_hours += cte.WEEK_DAYS_A_YEAR[day_type] / 365 / 24
total_values *= total_hours
total_internal_gain += internal_gain * total_values
@ -181,8 +179,8 @@ class InselMonthlyEnergyBalance:
ventilation_day += 0
infiltration_day += infiltration_value / 24
for day_type in schedule.day_types:
infiltration += infiltration_day * cte.DAYS_A_YEAR[day_type] / 365
ventilation += ventilation_day * cte.DAYS_A_YEAR[day_type] / 365
infiltration += infiltration_day * cte.WEEK_DAYS_A_YEAR[day_type] / 365
ventilation += ventilation_day * cte.WEEK_DAYS_A_YEAR[day_type] / 365
ventilation_infiltration = ventilation + infiltration
parameters.append(f'{ventilation_infiltration} % BP(18) #8 Minimum air change rate zone {i + 1} (ACH)')
@ -230,7 +228,6 @@ class InselMonthlyEnergyBalance:
parameters.append(thermal_boundary.parent_surface.short_wave_reflectance)
else:
parameters.append(0.0)
file = InselMonthlyEnergyBalance._add_block(file, i_block, custom_insel_block, inputs=inputs, parameters=parameters)
i_block = 20
@ -238,9 +235,8 @@ class InselMonthlyEnergyBalance:
parameters = ['12 % Monthly ambient temperature (degree Celsius)']
external_temperature = building.external_temperature[cte.MONTH]
for i in range(0, len(external_temperature)):
parameters.append(f'{i + 1} {external_temperature.at[i, weather_format]}')
parameters.append(f'{i + 1} {external_temperature[i]}')
file = InselMonthlyEnergyBalance._add_block(file, i_block, 'polyg', inputs=inputs, parameters=parameters)
@ -248,12 +244,11 @@ class InselMonthlyEnergyBalance:
inputs = ['1']
parameters = ['12 % Monthly sky temperature']
sky_temperature = Weather.sky_temperature(external_temperature[[weather_format]].to_numpy().T[0])
sky_temperature = Weather.sky_temperature(external_temperature)
for i, temperature in enumerate(sky_temperature):
parameters.append(f'{i + 1} {temperature}')
file = InselMonthlyEnergyBalance._add_block(file, i_block, 'polyg', inputs=inputs, parameters=parameters)
for i, surface in enumerate(surfaces):
i_block = 101 + i
inputs = ['1 % Monthly surface radiation (W/m2)']
@ -266,7 +261,7 @@ class InselMonthlyEnergyBalance:
global_irradiance = surface.global_irradiance[cte.MONTH]
for j in range(0, len(global_irradiance)):
parameters.append(f'{j + 1} '
f'{global_irradiance.at[j, radiation_calculation_method] / 24 / _NUMBER_DAYS_PER_MONTH[j]}')
f'{global_irradiance[j] / 24 / _NUMBER_DAYS_PER_MONTH[j]}')
else:
for j in range(0, 12):
parameters.append(f'{j + 1} 0.0')
@ -290,5 +285,4 @@ class InselMonthlyEnergyBalance:
f"'{str(insel_outputs_path)}' % File name",
"'*' % Fortran format"]
file = InselMonthlyEnergyBalance._add_block(file, i_block, 'WRITE', inputs=inputs, parameters=parameters)
return file

4
hub/exports/formats/simplified_radiosity_algorithm.py
View File

@ -66,8 +66,8 @@ class SimplifiedRadiosityAlgorithm:
else:
i = (total_days + day - 1) * 24 + hour - 1
representative_building = self._city.buildings[0]
content += f'{day} {month} {hour} {representative_building.global_horizontal[cte.HOUR].epw[i]} ' \
f'{representative_building.beam[cte.HOUR].epw[i]}\n'
content += f'{day} {month} {hour} {representative_building.global_horizontal[cte.HOUR][i]} ' \
f'{representative_building.beam[cte.HOUR][i]}\n'
with open(file, 'w', encoding='utf-8') as file:
file.write(content)

17
hub/helpers/constants.py
View File

@ -48,7 +48,7 @@ WEEK_DAYS = 'Weekdays'
WEEK_ENDS = 'Weekends'
ALL_DAYS = 'Alldays'
DAYS_A_MONTH = {'monday': [5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 5],
WEEK_DAYS_A_MONTH = {'monday': [5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 5],
'tuesday': [5, 4, 4, 4, 5, 4, 5, 4, 4, 5, 4, 4],
'wednesday': [5, 4, 4, 4, 5, 4, 4, 5, 4, 5, 4, 4],
'thursday': [4, 4, 5, 4, 5, 4, 4, 5, 4, 4, 5, 4],
@ -57,7 +57,7 @@ DAYS_A_MONTH = {'monday': [5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 5],
'sunday': [4, 4, 4, 5, 4, 4, 5, 4, 5, 4, 4, 5],
'holiday': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
DAYS_A_YEAR = {'monday': 51,
WEEK_DAYS_A_YEAR = {'monday': 51,
'tuesday': 50,
'wednesday': 50,
'thursday': 50,
@ -66,6 +66,19 @@ DAYS_A_YEAR = {'monday': 51,
'sunday': 52,
'holiday': 10}
DAYS_A_MONTH = {'January': 31,
'February': 28,
'March': 31,
'April': 30,
'May': 31,
'June': 30,
'July': 31,
'August': 31,
'September': 30,
'October': 31,
'November': 30,
'December': 31}
# data types
ANY_NUMBER = 'any_number'
FRACTION = 'fraction'

63
hub/helpers/monthly_values.py
View File

@ -3,55 +3,46 @@ Monthly values module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Pilar Monsalvete pilar_monsalvete@yahoo.es
"""
import calendar as cal
import pandas as pd
import numpy as np
import hub.helpers.constants as cte
class MonthlyValues:
"""
Monthly values class
"""
def __init__(self):
self._month_hour = None
def get_mean_values(self, values):
@staticmethod
def get_mean_values(values):
"""
Calculates the mean values for each month
:return: DataFrame(float)
Calculates the mean values for each month from a list with hourly values
:return: [float] x 12
:param values: [float] x 8760
"""
out = None
out = []
if values is not None:
if 'month' not in values.columns:
values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
out = values.groupby('month', as_index=False).mean()
del out['month']
for month in cte.DAYS_A_MONTH:
total = 0
for j in range(0, cte.DAYS_A_MONTH[month]):
for k in range(0, 24):
hour = k + 24 * j + cte.DAYS_A_MONTH[month] - 31
total += values[hour] / 24 / cte.DAYS_A_MONTH[month]
out.append(total)
return out
def get_total_month(self, values):
@staticmethod
def get_total_month(values):
"""
Calculates the total value for each month
:return: DataFrame(int)
:return: [float] x 12
:param values: [float] x 8760
"""
out = None
out = []
if values is not None:
if 'month' not in values.columns:
values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
out = pd.DataFrame(values).groupby('month', as_index=False).sum()
del out['month']
for month in cte.DAYS_A_MONTH:
total = 0
for j in range(0, cte.DAYS_A_MONTH[month]):
for k in range(0, 24):
hour = k + 24 * j + cte.DAYS_A_MONTH[month] - 31
total += values[hour]
out.append(total)
return out
@property
def month_hour(self):
"""
returns a DataFrame that has x values of the month number (January = 1, February = 2...),
being x the number of hours of the corresponding month
:return: DataFrame(int)
"""
array = []
for i in range(0, 12):
days_of_month = cal.monthrange(2015, i+1)[1]
total_hours = days_of_month * 24
array = np.concatenate((array, np.full(total_hours, i + 1)))
self._month_hour = pd.DataFrame(array, columns=['month'])
return self._month_hour

4
hub/helpers/peak_calculation/loads_calculation.py
View File

@ -132,7 +132,7 @@ class LoadsCalculation:
internal_load = cooling_load_occupancy_sensible + cooling_load_lighting + cooling_load_equipment_sensible
return internal_load
def get_radiation_load(self, irradiance_format, hour):
def get_radiation_load(self, hour):
"""
Calculates the radiation load
:return: int
@ -142,7 +142,7 @@ class LoadsCalculation:
for thermal_zone in internal_zone.thermal_zones:
for thermal_boundary in thermal_zone.thermal_boundaries:
for thermal_opening in thermal_boundary.thermal_openings:
radiation = thermal_boundary.parent_surface.global_irradiance[cte.HOUR][irradiance_format][hour]
radiation = thermal_boundary.parent_surface.global_irradiance[cte.HOUR][hour]
cooling_load_radiation += (
thermal_opening.area * (1 - thermal_opening.frame_ratio) * thermal_opening.g_value * radiation
)

14
hub/helpers/peak_loads.py
View File

@ -54,11 +54,13 @@ class PeakLoads:
"""
month = 1
peaks = [0 for _ in range(12)]
for i, value in enumerate(hourly_values):
print('hv', hourly_values)
for i in range(0, len(hourly_values)):
if _MONTH_STARTING_HOUR[month] <= i:
month += 1
if value > peaks[month-1]:
peaks[month-1] = value
if hourly_values[i] > peaks[month-1]:
peaks[month-1] = hourly_values[i]
print('peak', peaks)
return peaks
@property
@ -70,7 +72,7 @@ class PeakLoads:
if not self._can_be_calculated():
return None
monthly_heating_loads = []
ambient_temperature = self._building.external_temperature[cte.HOUR]['epw']
ambient_temperature = self._building.external_temperature[cte.HOUR]
for month in range(0, 12):
ground_temperature = self._building.ground_temperature[cte.MONTH]['2'][month]
heating_ambient_temperature = 100
@ -100,7 +102,7 @@ class PeakLoads:
if not self._can_be_calculated():
return None
monthly_cooling_loads = []
ambient_temperature = self._building.external_temperature[cte.HOUR]['epw']
ambient_temperature = self._building.external_temperature[cte.HOUR]
for month in range(0, 12):
ground_temperature = self._building.ground_temperature[cte.MONTH]['2'][month]
cooling_ambient_temperature = -100
@ -118,7 +120,7 @@ class PeakLoads:
cooling_load_transmitted = loads.get_cooling_transmitted_load(cooling_ambient_temperature, ground_temperature)
cooling_load_renovation_sensible = loads.get_cooling_ventilation_load_sensible(cooling_ambient_temperature)
cooling_load_internal_gains_sensible = loads.get_internal_load_sensible()
cooling_load_radiation = loads.get_radiation_load('sra', cooling_calculation_hour)
cooling_load_radiation = loads.get_radiation_load(cooling_calculation_hour)
cooling_load_sensible = cooling_load_transmitted + cooling_load_renovation_sensible - cooling_load_radiation \
- cooling_load_internal_gains_sensible

147
hub/imports/energy_systems/air_source_hp_parameters.py
View File

@ -1,147 +0,0 @@
"""
AirSourceHeatPumpParameters import the heat pump information
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Peter Yefi peteryefi@gmail.comCode
contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import io
import itertools
from typing import List
from typing import Dict
import pandas as pd
import numpy as np
from scipy.optimize import curve_fit
from hub.city_model_structure.energy_systems.air_source_hp import AirSourceHP
from hub.city_model_structure.energy_system import EnergySystem
class AirSourceHeatPumpParameters:
"""
AirSourceHeatPumpParameters class
"""
def __init__(self, city, base_path):
self._city = city
self._base_path = (base_path / 'heat_pumps/air_source.xlsx').resolve()
def _read_file(self) -> Dict:
"""
reads xlsx file containing the heat pump information
into a dictionary
:return : Dict
"""
with open(self._base_path, 'rb') as xls:
xl_file = pd.read_excel(io.BytesIO(xls.read()), sheet_name=None)
cooling_data = {}
heating_data = {}
for sheet, _ in xl_file.items():
if 'Summary' in sheet:
continue
# Remove nan rows and columns and extract cooling and heating data
# for each sheet
df = xl_file[sheet].dropna(axis=1, how='all')
cooling_df = df.iloc[4:34, 0:8]
heating_df = df.iloc[4:29, 8:20]
# extract the data into dictionaries each sheet is a key entry in the
# dictionary
cooling_data[sheet] = {}
heating_data[sheet] = {}
i = 0
# for each sheet extract data for twout/Ta.RU temperatures. Thus, the twout
# temp is the key for the values of pf,pa,qw data
while i < 25:
cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
heating_data[sheet][heating_df.iloc[i][0]] = heating_df.iloc[i + 1:i + 4, 2:8].values.tolist()
i = i + 5
# extract the last cooling data
cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
return {"cooling": cooling_data, "heating": heating_data}
def enrich_city(self):
"""
Enriches the city with information from file
"""
heat_pump_data = self._read_file()
for (k_cool, v_cool), (_, v_heat) in zip(heat_pump_data["cooling"].items(), heat_pump_data["heating"].items()):
heat_pump = AirSourceHP()
heat_pump.model = k_cool
h_data = self._extract_heat_pump_data(v_heat)
c_data = self._extract_heat_pump_data(v_cool)
heat_pump.cooling_capacity = c_data[0]
heat_pump.cooling_comp_power = c_data[1]
heat_pump.cooling_capacity_coff = self._compute_coefficients(c_data, "cool")
heat_pump.heating_capacity = h_data[0]
heat_pump.heating_comp_power = h_data[1]
heat_pump.heating_capacity_coff = self._compute_coefficients(h_data)
energy_system = EnergySystem(f'{heat_pump.model} capacity heat pump', [])
energy_system.air_source_hp = heat_pump
self._city.add_city_object(energy_system)
return self._city
@staticmethod
def _extract_heat_pump_data(heat_pump_capacity_data: Dict) -> [List, List]:
"""
Fetches a list of metric based data for heat pump for various temperature,
e.g. cooling capacity data for 12 capacity heat pump
for 6,7,8,9,10 and 11 degree Celsius
:param heat_pump_capacity_data: the heat pump capacity data from the
which the metric specific data is fetched: {List}
:return: List
"""
cooling_heating_capacity_data = []
compressor_power_data = []
for _, metric_data in heat_pump_capacity_data.items():
cooling_heating_capacity_data.append(metric_data[0])
compressor_power_data.append(metric_data[1])
return [cooling_heating_capacity_data, compressor_power_data]
def _compute_coefficients(self, heat_pump_data: List, data_type="heat") -> List[float]:
"""
Compute heat output and electrical demand coefficients
from heating and cooling performance data
:param heat_pump_data: a list of heat pump data. e.g. cooling capacity
:param data_type: string to indicate if data is cooling performance data
or heating performance data
:return: Tuple[Dict, Dict]
"""
# Determine the recurrence of temperature values. 6 repetitions for
# cooling performance and 5 repetition for heating performance
temp_multiplier = 5 if data_type == "heat" else 6
out_temp = [25, 30, 32, 35, 40, 45] * temp_multiplier
heat_x_values = np.repeat([-5, 0, 7, 10, 15], 6)
cool_x_values = np.repeat([6, 7, 8, 9, 10, 11], 6)
x_values = heat_x_values if data_type == "heat" else cool_x_values
x_values = x_values.tolist()
# convert list of lists to one list
hp_data = [i / j for i, j in
zip(list(itertools.chain.from_iterable(heat_pump_data[0])),
list(itertools.chain.from_iterable(heat_pump_data[1])))]
# Compute heat output coefficients
popt, _ = curve_fit(self._objective_function, [x_values, out_temp], hp_data)
return popt.tolist()
@staticmethod
def _objective_function(xdata: List, a1: float, a2: float, a3: float, a4: float, a5: float, a6: float) -> float:
"""
Objective function for computing coefficients
:param xdata:
:param a1: float
:param a2: float
:param a3: float
:param a4: float
:param a5: float
:param a6: float
:return:
"""
x, y = xdata
return (a1 * x ** 2) + (a2 * x) + (a3 * x * y) + (a4 * y) + (a5 * y ** 2) + a6

0
hub/imports/energy_systems/helpers/__init__.py
View File

29
hub/imports/energy_systems/helpers/energy_systems_helper.py
View File

@ -1,29 +0,0 @@
"""
Energy systems helper
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
from hub.helpers import constants as cte
class EnergySystemsHelper:
"""
EnergySystems helper
"""
_montreal_custom_fuel_to_hub_fuel = {
'gas': cte.GAS,
'electricity': cte.ELECTRICITY,
'renewable': cte.RENEWABLE
}
@staticmethod
def montreal_custom_fuel_to_hub_fuel(fuel):
"""
Get hub fuel from montreal_custom catalog fuel
:param fuel: str
:return: str
"""
return EnergySystemsHelper._montreal_custom_fuel_to_hub_fuel[fuel]

1
hub/imports/energy_systems/montreal_custom_energy_system_parameters.py
View File

@ -19,7 +19,6 @@ from hub.city_model_structure.energy_systems.generation_system import Generation
from hub.city_model_structure.energy_systems.distribution_system import DistributionSystem
from hub.city_model_structure.energy_systems.emission_system import EmissionSystem
from hub.helpers.dictionaries import Dictionaries
from hub.imports.energy_systems.helpers.energy_systems_helper import EnergySystemsHelper
class MontrealCustomEnergySystemParameters:

173
hub/imports/energy_systems/water_to_water_hp_parameters.py
View File

@ -1,173 +0,0 @@
"""
WaterToWaterHPParameters import the heat pump information
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Peter Yefi peteryefi@gmail.com
"""
from typing import Dict
from typing import List
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from hub.city_model_structure.energy_system import EnergySystem
from hub.city_model_structure.energy_systems.water_to_water_hp import WaterToWaterHP
class WaterToWaterHPParameters:
"""
WaterToWaterHPParameters class
"""
def __init__(self, city, base_path):
self._city = city
self._base_path = (base_path / 'heat_pumps/water_to_water.xlsx').resolve()
def _read_file(self) -> Dict:
# todo: this method is keeping the excel file open and should be either corrected or removed
xl_file = pd.ExcelFile(self._base_path)
heat_pump_dfs = {sheet_name: xl_file.parse(sheet_name)
for sheet_name in xl_file.sheet_names}
hp_data = {}
flow_rates = {
'156': [2.84, 4.23, 5.68],
'256': [4.73, 7.13, 9.446],
'335': [6.62, 9.97, 12.93],
}
for sheet, _ in heat_pump_dfs.items():
df = heat_pump_dfs[sheet].dropna(axis=1, how='all')
df = df.iloc[3:, 6:35]
if '156' in sheet:
hp_data[sheet] = self._extract_required_hp_data(df, [0, 10, 25, 40, 55, 67], flow_rates['156'])
elif '256' in sheet:
hp_data[sheet] = self._extract_required_hp_data(df, [0, 9, 24, 39, 54, 66], flow_rates['256'])
elif '335' in sheet:
hp_data[sheet] = self._extract_required_hp_data(df, [0, 11, 26, 41, 56, 69], flow_rates['335'])
return hp_data
def _extract_required_hp_data(self, dataframe, ranges, flow_rates):
"""
Extracts 156 Kw water to water heat pump data
:param dataframe: dataframe containing all data
:param ranges: the range of values to extract
:param flow_rates: the flow rates of water through pump
:return: Dict
"""
# extract data rows and columns
data = {'tc': self._extract_hp_data(dataframe, [1, 11, 21], ranges),
'pd': self._extract_hp_data(dataframe, [2, 12, 22], ranges),
'lwt': self._extract_hp_data(dataframe, [5, 15, 25], ranges),
'fr': (self._extract_flow_and_ewt(dataframe, ranges, [1, 11, 21], flow_rates))[0],
'ewt': (self._extract_flow_and_ewt(dataframe, ranges, [1, 11, 21], flow_rates))[1]}
# range values for extracting data
return data
@staticmethod
def _extract_hp_data(df, columns, ranges):
"""
Extract variable specific (LWT, PD or TC) data from water to water hp
:param df: the dataframe
:param columns: the columns to extract data from
:param ranges: the range of values to extract
:return: List
"""
data = pd.concat([df.iloc[ranges[0]:ranges[1], columns[0]], df.iloc[ranges[0]:ranges[1], columns[1]]])
data = pd.concat([df.iloc[ranges[0]:ranges[1], columns[2]], data])
for i in range(1, 5):
data = pd.concat([df.iloc[ranges[i]:ranges[i + 1], columns[0]], data])
data = pd.concat([df.iloc[ranges[i]:ranges[i + 1], columns[1]], data])
data = pd.concat([df.iloc[ranges[i]:ranges[i + 1], columns[2]], data])
return data.dropna().values.tolist()
@staticmethod
def _extract_flow_and_ewt(df, ranges, columns, flow_rates):
"""
Create the flow and ewt data based on the length of the various
columns for the variables being extracted
:param df: the dataframe
:param ranges: the range of values to extract
:param columns: the columns to extract data from
:param flow_rates: flow rate values
:return:
"""
ewt_values = [-1.111111111, 4.444444444, 10, 15.55555556, 21.11111111]
length = [len(df.iloc[ranges[0]:ranges[1], columns[0]].dropna()),
len(df.iloc[ranges[0]:ranges[1], columns[1]].dropna()),
len(df.iloc[ranges[0]:ranges[1], columns[2]].dropna())]
ewt_data = np.repeat(ewt_values[0], sum(length))
flow_rates_data = np.repeat(flow_rates, length)
for i in range(1, 5):
length = [len(df.iloc[ranges[i]:ranges[i + 1], columns[0]].dropna()),
len(df.iloc[ranges[i]:ranges[i + 1], columns[1]].dropna()),
len(df.iloc[ranges[i]:ranges[i + 1], columns[2]].dropna())]
flow_rates_data = np.append(flow_rates_data, np.repeat(flow_rates, length))
ewt_data = np.append(ewt_data, np.repeat(ewt_values[i], sum(length)))
return flow_rates_data.tolist(), ewt_data.tolist()
def enrich_city(self):
"""
Enriches the city with information from file
"""
heap_pump_data = self._read_file()
for model, data in heap_pump_data.items():
heat_pump = WaterToWaterHP()
heat_pump.model = model.strip()
heat_pump.total_cooling_capacity = data['tc']
heat_pump.power_demand = data['pd']
heat_pump.flow_rate = data['fr']
heat_pump.entering_water_temp = data['ewt']
heat_pump.leaving_water_temp = data['lwt']
heat_pump.power_demand_coff = self._compute_coefficients(data)
energy_system = EnergySystem(heat_pump.model, [])
energy_system.water_to_water_hp = heat_pump
self._city.add_city_object(energy_system)
return self._city
def _compute_coefficients(self, heat_pump_data: Dict) -> List[float]:
"""
Compute heat output and electrical demand coefficients
from heating performance data
:param heat_pump_data: a dictionary of heat pump data.
:return: Tuple[Dict, Dict]
"""
demand = [i / j for i, j in zip(heat_pump_data['tc'], heat_pump_data['pd'])]
# Compute heat output coefficients
popt, _ = curve_fit(
self._objective_function, [heat_pump_data['ewt'], heat_pump_data['lwt'], heat_pump_data['fr']], demand
)
return popt.tolist()
@staticmethod
def _objective_function(xdata: List, a1: float, a2: float, a3: float, a4: float, a5: float, a6: float,
a7: float, a8: float, a9: float, a10: float, a11: float) -> float:
"""
Objective function for computing coefficients
:param xdata:
:param a1: float
:param a2: float
:param a3: float
:param a4: float
:param a5: float
:param a6: float
:param a7: float
:param a8: float
:param a9: float
:param a10: float
:param a11: float
:return:
"""
x, y, t = xdata
return (a1 * x ** 2) + (a2 * x) + (a3 * y ** 2) + (a4 * y) + (a5 * t ** 2) + (a6 * t) + (a7 * x * y) + (
a8 * x * t) + (a9 * y * t) + (a10 * x * y * t) + a11

20
hub/imports/energy_systems_factory.py
View File

@ -8,9 +8,7 @@ Code contributors: Peter Yefi peteryefi@gmail.com
from pathlib import Path
from hub.helpers.utils import validate_import_export_type
from hub.imports.energy_systems.air_source_hp_parameters import AirSourceHeatPumpParameters
from hub.imports.energy_systems.montreal_custom_energy_system_parameters import MontrealCustomEnergySystemParameters
from hub.imports.energy_systems.water_to_water_hp_parameters import WaterToWaterHPParameters
class EnergySystemsFactory:
@ -26,24 +24,6 @@ class EnergySystemsFactory:
self._city = city
self._base_path = base_path
def _air_source_hp(self):
"""
Enrich the city by using xlsx heat pump information
"""
AirSourceHeatPumpParameters(self._city, self._base_path).enrich_city()
self._city.level_of_detail.energy_systems = 0
for building in self._city.buildings:
building.level_of_detail.energy_systems = 0
def _water_to_water_hp(self):
"""
Enrich the city by using water to water heat pump information
"""
WaterToWaterHPParameters(self._city, self._base_path).enrich_city()
self._city.level_of_detail.energy_systems = 0
for building in self._city.buildings:
building.level_of_detail.energy_systems = 0
def _montreal_custom(self):
"""
Enrich the city by using montreal custom energy systems catalog information

116
hub/imports/geometry/gpandas.py
View File

@ -1,116 +0,0 @@
"""
gpandas module parses geopandas input table and import the geometry into the city model structure
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder: Milad Aghamohamadnia --- milad.aghamohamadnia@concordia.ca
"""
import trimesh
import trimesh.exchange.load
import trimesh.geometry
import trimesh.creation
import trimesh.repair
from shapely.geometry import Point
from shapely.geometry import Polygon as ShapelyPoly
from trimesh import Scene
from hub.city_model_structure.attributes.polygon import Polygon
from hub.city_model_structure.building import Building
from hub.city_model_structure.building_demand.surface import Surface
from hub.city_model_structure.city import City
import hub.helpers.constants as cte
class GPandas:
"""
GeoPandas class
"""
def __init__(self, dataframe, srs_name='EPSG:26911'):
"""_summary_
Arguments:
dataframe {Geopandas.Dataframe} -- input geometry data in geopandas table
Keyword Arguments:
srs_name {str} -- coordinate system of coordinate system (default: {'EPSG:26911'})
"""
self._srs_name = srs_name
self._city = None
self._scene = dataframe
self._scene = self._scene.to_crs(self._srs_name)
min_x, min_y, max_x, max_y = self._scene.total_bounds
self._lower_corner = [min_x, min_y, 0]
self._upper_corner = [max_x, max_y, 0]
@property
def scene(self) -> Scene:
"""
Get GeoPandas scene
"""
return self._scene
@property
def city(self) -> City:
"""
Get city out of a GeoPandas Table
"""
if self._city is None:
self._city = City(self._lower_corner, self._upper_corner, self._srs_name)
lod = 0
for _, bldg in self._scene.iterrows():
polygon = bldg.geometry
height = float(bldg['height'])
building_mesh = trimesh.creation.extrude_polygon(polygon, height)
trimesh.repair.fill_holes(building_mesh)
trimesh.repair.fix_winding(building_mesh)
year_of_construction = int(bldg['year_built'])
name = bldg['name']
lod = 1
if year_of_construction > 2000:
function = cte.RESIDENTIAL
else:
function = cte.INDUSTRY
surfaces = []
for _, face in enumerate(building_mesh.faces):
points = []
for vertex_index in face:
points.append(building_mesh.vertices[vertex_index])
solid_polygon = Polygon(points)
perimeter_polygon = solid_polygon
surface = Surface(solid_polygon, perimeter_polygon)
surfaces.append(surface)
building = Building(name, surfaces, year_of_construction, function, terrains=None)
self._city.add_city_object(building)
self._city.level_of_detail.geometry = lod
for building in self._city.buildings:
building.level_of_detail.geometry = lod
return self._city
@staticmethod
def resize_polygon(poly, factor=0.10, expand=False) -> ShapelyPoly:
"""
returns the shapely polygon which is smaller or bigger by passed factor.
Arguments:
poly {shapely.geometry.Polygon} -- an input geometry in shapely polygon format
Keyword Arguments:
factor {float} -- factor of expansion (default: {0.10})
expand {bool} -- If expand = True , then it returns bigger polygon, else smaller (default: {False})
Returns:
{shapely.geometry.Polygon} -- output geometry in shapely polygon format
"""
xs = list(poly.exterior.coords.xy[0])
ys = list(poly.exterior.coords.xy[1])
x_center = 0.5 * min(xs) + 0.5 * max(xs)
y_center = 0.5 * min(ys) + 0.5 * max(ys)
min_corner = Point(min(xs), min(ys))
center = Point(x_center, y_center)
shrink_distance = center.distance(min_corner) * factor
if expand:
poly_resized = poly.buffer(shrink_distance) # expand
else:
poly_resized = poly.buffer(-shrink_distance) # shrink
return poly_resized

12
hub/imports/geometry_factory.py
View File

@ -4,13 +4,11 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
"""
import geopandas
from hub.city_model_structure.city import City
from hub.helpers.utils import validate_import_export_type
from hub.imports.geometry.citygml import CityGml
from hub.imports.geometry.geojson import Geojson
from hub.imports.geometry.gpandas import GPandas
from hub.imports.geometry.obj import Obj
@ -56,16 +54,6 @@ class GeometryFactory:
"""
return Obj(self._path).city
@property
def _gpandas(self) -> City:
"""
Enrich the city by using GeoPandas information as data source
:return: City
"""
if self._data_frame is None:
self._data_frame = geopandas.read_file(self._path)
return GPandas(self._data_frame).city
@property
def _geojson(self) -> City:
"""

40
hub/imports/results/insel_heatpump_energy_demand.py
View File

@ -1,40 +0,0 @@
"""
Insel Heap pump energy demand and fossil fuel consumption
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Concordia CERC group
Project Coder peter.yefi@gmail.cm
"""
from pathlib import Path
import pandas as pd
class InselHeatPumpEnergyDemand:
"""
Import Energy demand and fossil fuel consumption results
"""
def __init__(self, city, base_path, hp_model):
"""
:param city: the city
:param base_path: the insel simulation output file
:param hp_model: the heatpump model for both air source and water to water
"""
self._city = city
self._hp_model = hp_model
with open(Path(base_path).resolve(), 'r', encoding='utf8') as csv_file:
df = pd.read_csv(csv_file)
self._monthly_electricity_demand = df.iloc[:, 1]
self._monthly_fossil_fuel_consumption = df.iloc[:, 2]
def enrich(self):
"""
Enrich the city with the heat pump information
"""
for energy_system in self._city.energy_systems:
if energy_system.air_source_hp is not None:
if energy_system.air_source_hp.model == self._hp_model:
energy_system.air_source_hp.hp_monthly_fossil_consumption = self._monthly_fossil_fuel_consumption
if energy_system.water_to_water_hp is not None:
if energy_system.water_to_water_hp.model == self._hp_model:
energy_system.water_to_water_hp.hp_monthly_electricity_demand = self._monthly_electricity_demand

49
hub/imports/results/insel_monthly_energry_balance.py
View File

@ -4,9 +4,9 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Guillermo.GutierrezMorote@concordia.ca
"""
from pathlib import Path
import csv
import pandas as pd
import hub.helpers.constants as cte
@ -34,11 +34,9 @@ class InselMonthlyEnergyBalance:
demand[i] = str(aux)
else:
demand[i] = '0'
heating.append(demand[0])
cooling.append(demand[1])
monthly_heating = pd.DataFrame(heating, columns=[cte.INSEL_MEB]).astype(float)
monthly_cooling = pd.DataFrame(cooling, columns=[cte.INSEL_MEB]).astype(float)
return monthly_heating, monthly_cooling
heating.append(float(demand[0]))
cooling.append(float(demand[1]))
return heating, cooling
def _dhw_and_electric_demand(self):
for building in self._city.buildings:
@ -52,7 +50,7 @@ class InselMonthlyEnergyBalance:
else:
thermal_zone = building.internal_zones[0].thermal_zones[0]
area = thermal_zone.total_floor_area
cold_water = building.cold_water_temperature[cte.MONTH]['epw']
cold_water = building.cold_water_temperature[cte.MONTH]
peak_flow = thermal_zone.domestic_hot_water.peak_flow
service_temperature = thermal_zone.domestic_hot_water.service_temperature
lighting_density = thermal_zone.lighting.density
@ -68,7 +66,7 @@ class InselMonthlyEnergyBalance:
for value in schedule.values:
total_day += value
for day_type in schedule.day_types:
total_lighting += total_day * cte.DAYS_A_MONTH[day_type][month] * lighting_density
total_lighting += total_day * cte.WEEK_DAYS_A_MONTH[day_type][month] * lighting_density
lighting_demand.append(total_lighting * area)
for schedule in thermal_zone.appliances.schedules:
@ -76,7 +74,7 @@ class InselMonthlyEnergyBalance:
for value in schedule.values:
total_day += value
for day_type in schedule.day_types:
total_appliances += total_day * cte.DAYS_A_MONTH[day_type][month] * appliances_density
total_appliances += total_day * cte.WEEK_DAYS_A_MONTH[day_type][month] * appliances_density
appliances_demand.append(total_appliances * area)
for schedule in thermal_zone.domestic_hot_water.schedules:
@ -87,26 +85,15 @@ class InselMonthlyEnergyBalance:
demand = (
peak_flow * cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY * (service_temperature - cold_water[month])
)
total_dhw_demand += total_day * cte.DAYS_A_MONTH[day_type][month] * demand
total_dhw_demand += total_day * cte.WEEK_DAYS_A_MONTH[day_type][month] * demand
domestic_hot_water_demand.append(total_dhw_demand * area)
building.domestic_hot_water_heat_demand[cte.MONTH] = pd.DataFrame(domestic_hot_water_demand,
columns=[cte.INSEL_MEB])
yearly_domestic_hot_water_demand = [sum(domestic_hot_water_demand)]
building.domestic_hot_water_heat_demand[cte.YEAR] = pd.DataFrame(yearly_domestic_hot_water_demand,
columns=[cte.INSEL_MEB])
building.lighting_electrical_demand[cte.MONTH] = pd.DataFrame(lighting_demand, columns=[cte.INSEL_MEB])
yearly_lighting_electrical_demand = [sum(lighting_demand)]
building.lighting_electrical_demand[cte.YEAR] = pd.DataFrame(
yearly_lighting_electrical_demand,
columns=[cte.INSEL_MEB]
)
building.appliances_electrical_demand[cte.MONTH] = pd.DataFrame(appliances_demand, columns=[cte.INSEL_MEB])
yearly_appliances_electrical_demand = [sum(appliances_demand)]
building.appliances_electrical_demand[cte.YEAR] = pd.DataFrame(
yearly_appliances_electrical_demand,
columns=[cte.INSEL_MEB]
)
building.domestic_hot_water_heat_demand[cte.MONTH] = domestic_hot_water_demand
building.domestic_hot_water_heat_demand[cte.YEAR] = [sum(domestic_hot_water_demand)]
building.lighting_electrical_demand[cte.MONTH] = lighting_demand
building.lighting_electrical_demand[cte.YEAR] = [sum(lighting_demand)]
building.appliances_electrical_demand[cte.MONTH] = appliances_demand
building.appliances_electrical_demand[cte.YEAR] = [sum(appliances_demand)]
def enrich(self):
"""
@ -118,10 +105,6 @@ class InselMonthlyEnergyBalance:
insel_output_file_path = Path(self._base_path / file_name).resolve()
if insel_output_file_path.is_file():
building.heating_demand[cte.MONTH], building.cooling_demand[cte.MONTH] = self._conditioning_demand(insel_output_file_path)
building.heating_demand[cte.YEAR] = pd.DataFrame(
[building.heating_demand[cte.MONTH][cte.INSEL_MEB].astype(float).sum()], columns=[cte.INSEL_MEB]
)
building.cooling_demand[cte.YEAR] = pd.DataFrame(
[building.cooling_demand[cte.MONTH][cte.INSEL_MEB].astype(float).sum()], columns=[cte.INSEL_MEB]
)
building.heating_demand[cte.YEAR] = [sum(building.heating_demand[cte.MONTH])]
building.cooling_demand[cte.YEAR] = [sum(building.cooling_demand[cte.MONTH])]
self._dhw_and_electric_demand()

82
hub/imports/results/simplified_radiosity_algorithm.py
View File

@ -4,10 +4,10 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Guillermo.GutierrezMorote@concordia.ca
"""
import calendar as cal
import pandas as pd
import numpy as np
import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
class SimplifiedRadiosityAlgorithm:
@ -19,54 +19,11 @@ class SimplifiedRadiosityAlgorithm:
self._city = city
self._base_path = base_path
self._input_file_path = (self._base_path / f'{self._city.name}_sra_SW.out').resolve()
self._month_hour = self._month_hour_data_frame
self._results = self._read_results()
self._radiation_list = []
@property
def _month_hour_data_frame(self):
array = []
for i in range(0, 12):
days_of_month = cal.monthrange(2015, i+1)[1]
total_hours = days_of_month * 24
array = np.concatenate((array, np.full(total_hours, i + 1)))
return pd.DataFrame(array, columns=[cte.MONTH])
def _get_monthly_values(self, values):
out = None
if values is not None:
if cte.MONTH not in values.columns:
values = pd.concat([self._month_hour, pd.DataFrame(values)], axis=1)
out = values.groupby(cte.MONTH, as_index=False).sum()
del out[cte.MONTH]
return out
@staticmethod
def _get_yearly_values(values):
return [values.sum()]
def _read_results(self):
try:
return pd.read_csv(self._input_file_path, sep='\s+', header=0)
self._results = pd.read_csv(self._input_file_path, sep='\s+', header=0).to_dict(orient='list')
except FileNotFoundError as err:
raise FileNotFoundError('No SRA output file found') from err
@property
def _radiation(self) -> []:
if len(self._radiation_list) == 0:
id_building = ''
header_building = []
for column in self._results.columns.values:
if id_building != column.split(':')[1]:
id_building = column.split(':')[1]
if len(header_building) > 0:
self._radiation_list.append(pd.concat([self._month_hour, self._results[header_building]], axis=1))
header_building = [column]
else:
header_building.append(column)
self._radiation_list.append(pd.concat([self._month_hour, self._results[header_building]], axis=1))
return self._radiation_list
def enrich(self):
"""
saves in building surfaces the correspondent irradiance at different time-scales depending on the mode
@ -74,28 +31,21 @@ class SimplifiedRadiosityAlgorithm:
specific building values
:return: none
"""
for radiation in self._radiation:
city_object_name = radiation.columns.values.tolist()[1].split(':')[1]
_irradiance = {}
for key in self._results:
header_name = key.split(':')
result = self._results[key]
city_object_name = header_name[1]
building = self._city.city_object(city_object_name)
for column in radiation.columns.values:
if column == cte.MONTH:
continue
header_id = column
surface_id = header_id.split(':')[2]
surface_id = header_name[2]
surface = building.surface_by_id(surface_id)
new_value = pd.DataFrame(radiation[[header_id]].to_numpy(), columns=[cte.SRA])
month_new_value = self._get_monthly_values(new_value)
if cte.MONTH not in surface.global_irradiance:
surface.global_irradiance[cte.MONTH] = month_new_value
else:
pd.concat([surface.global_irradiance[cte.MONTH], month_new_value], axis=1)
if cte.HOUR not in surface.global_irradiance:
surface.global_irradiance[cte.HOUR] = new_value
else:
pd.concat([surface.global_irradiance[cte.HOUR], new_value], axis=1)
if cte.YEAR not in surface.global_irradiance:
surface.global_irradiance[cte.YEAR] = pd.DataFrame(SimplifiedRadiosityAlgorithm._get_yearly_values(new_value),
columns=[cte.SRA])
monthly_result = MonthlyValues.get_total_month(result)
yearly_result = [sum(result)]
_irradiance[cte.YEAR] = yearly_result
_irradiance[cte.MONTH] = monthly_result
_irradiance[cte.HOUR] = result
surface.global_irradiance = _irradiance
self._city.level_of_detail.surface_radiation = 2
for building in self._city.buildings:
building.level_of_detail.surface_radiation = 2

8
hub/imports/results_factory.py
View File

@ -8,7 +8,6 @@ Code contributors: Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concord
from pathlib import Path
from hub.helpers.utils import validate_import_export_type
from hub.imports.results.insel_heatpump_energy_demand import InselHeatPumpEnergyDemand
from hub.imports.results.insel_monthly_energry_balance import InselMonthlyEnergyBalance
from hub.imports.results.simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
@ -41,13 +40,6 @@ class ResultFactory:
"""
SimplifiedRadiosityAlgorithm(self._city, self._base_path).enrich()
def _heat_pump(self):
"""
Enrich the city (energy system specifically) with heat pump insel simulation
results
"""
InselHeatPumpEnergyDemand(self._city, self._base_path, self._hp_model).enrich()
def _insel_monthly_energy_balance(self):
"""
Enrich the city with insel monthly energy balance results

2
hub/imports/usage/comnet_usage_parameters.py
View File

@ -104,7 +104,7 @@ class ComnetUsageParameters:
_domestic_hot_water.service_temperature = archetype.domestic_hot_water.service_temperature
peak_flow = None
if len(cold_water_temperature) > 0:
cold_temperature = cold_water_temperature[cte.YEAR]['epw']
cold_temperature = cold_water_temperature[cte.YEAR][0]
peak_flow = 0
if (archetype.domestic_hot_water.service_temperature - cold_temperature) > 0:
peak_flow = archetype.domestic_hot_water.density / cte.WATER_DENSITY / cte.WATER_HEAT_CAPACITY \

2
hub/imports/usage/eilat_usage_parameters.py
View File

@ -104,7 +104,7 @@ class EilatUsageParameters:
_domestic_hot_water.service_temperature = archetype.domestic_hot_water.service_temperature
peak_flow = None
if len(cold_water_temperature) > 0:
cold_temperature = cold_water_temperature[cte.YEAR]['epw']
cold_temperature = cold_water_temperature[cte.YEAR][0]
peak_flow = 0
if (archetype.domestic_hot_water.service_temperature - cold_temperature) > 0:
peak_flow = archetype.domestic_hot_water.density / cte.WATER_DENSITY / cte.WATER_HEAT_CAPACITY \

2
hub/imports/usage/nrcan_usage_parameters.py
View File

@ -130,7 +130,7 @@ class NrcanUsageParameters:
_domestic_hot_water.service_temperature = archetype.domestic_hot_water.service_temperature
density = None
if len(cold_water_temperature) > 0:
cold_temperature = cold_water_temperature[cte.YEAR]['epw']
cold_temperature = cold_water_temperature[cte.YEAR][0]
density = (
archetype.domestic_hot_water.peak_flow * cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY *
(archetype.domestic_hot_water.service_temperature - cold_temperature)

93
hub/imports/weather/epw_weather_parameters.py
View File

@ -4,14 +4,15 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import logging
import sys
from pathlib import Path
import pandas as pd
import requests
import pandas as pd
import hub.helpers.constants as cte
from hub.helpers.monthly_values import MonthlyValues
from hub.city_model_structure.city import City
from hub.imports.weather.helpers.weather import Weather as wh
@ -75,6 +76,25 @@ class EpwWeatherParameters:
'snow_depth_cm',
'days_since_last_snowfall', 'albedo', 'liquid_precipitation_depth_mm',
'liquid_precipitation_quality_hr'])
number_invalid_records = self._weather_values[
self._weather_values.dry_bulb_temperature_c == 99.9].count().dry_bulb_temperature_c
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 99.9) in dry bulb temperature\n')
number_invalid_records = self._weather_values[
self._weather_values.global_horizontal_radiation_wh_m2 == 9999].count().global_horizontal_radiation_wh_m2
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 9999) in global horizontal radiation\n')
number_invalid_records = self._weather_values[
self._weather_values.diffuse_horizontal_radiation_wh_m2 == 9999].count().diffuse_horizontal_radiation_wh_m2
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 9999) in diffuse horizontal radiation\n')
number_invalid_records = self._weather_values[
self._weather_values.direct_normal_radiation_wh_m2 == 9999].count().direct_normal_radiation_wh_m2
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 9999) in direct horizontal radiation\n')
self._weather_values = self._weather_values.to_dict(orient='list')
except SystemExit:
sys.stderr.write(f'Error: wrong formatting of weather file {self._path}\n')
sys.exit()
@ -82,72 +102,29 @@ class EpwWeatherParameters:
building.ground_temperature[cte.MONTH] = ground_temperature_from_file
ground_temperature = {}
for ground_temperature_set in building.ground_temperature[cte.MONTH]:
temperature = 0
for value in building.ground_temperature[cte.MONTH][ground_temperature_set]:
temperature += value / 12
temperature = sum(building.ground_temperature[cte.MONTH][ground_temperature_set]) / 12
ground_temperature[ground_temperature_set] = [temperature]
building.ground_temperature[cte.YEAR] = ground_temperature
if cte.HOUR in building.external_temperature:
del building.external_temperature[cte.HOUR]
new_value = pd.DataFrame(self._weather_values[['dry_bulb_temperature_c']].to_numpy(), columns=['epw'])
number_invalid_records = new_value[new_value.epw == 99.9].count().epw
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 99.9) in dry bulb temperature\n')
if cte.HOUR not in building.external_temperature:
building.external_temperature[cte.HOUR] = new_value
else:
pd.concat([building.external_temperature[cte.HOUR], new_value], axis=1)
# new_value = pd.DataFrame(self._weather_values[['dry_bulb_temperature_c']].to_numpy(), columns=['epw'])
# number_invalid_records = new_value[new_value.epw == 99.9].count().epw
building.external_temperature[cte.HOUR] = self._weather_values['dry_bulb_temperature_c']
building.global_horizontal[cte.HOUR] = self._weather_values['global_horizontal_radiation_wh_m2']
building.diffuse[cte.HOUR] = self._weather_values['diffuse_horizontal_radiation_wh_m2']
building.beam[cte.HOUR] = self._weather_values['direct_normal_radiation_wh_m2']
building.cold_water_temperature[cte.HOUR] = wh().cold_water_temperature(building.external_temperature[cte.HOUR])
new_value = pd.DataFrame(self._weather_values[['global_horizontal_radiation_wh_m2']].to_numpy(), columns=['epw'])
number_invalid_records = new_value[new_value.epw == 9999].count().epw
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 9999) in global horizontal radiation\n')
if cte.HOUR not in building.global_horizontal:
building.global_horizontal[cte.HOUR] = new_value
else:
pd.concat([building.global_horizontal[cte.HOUR], new_value], axis=1)
new_value = pd.DataFrame(self._weather_values[['diffuse_horizontal_radiation_wh_m2']].to_numpy(), columns=['epw'])
number_invalid_records = new_value[new_value.epw == 9999].count().epw
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 9999) in diffuse horizontal radiation\n')
if cte.HOUR not in building.diffuse:
building.diffuse[cte.HOUR] = new_value
else:
pd.concat([building.diffuse[cte.HOUR], new_value], axis=1)
new_value = pd.DataFrame(self._weather_values[['direct_normal_radiation_wh_m2']].to_numpy(), columns=['epw'])
number_invalid_records = new_value[new_value.epw == 9999].count().epw
if number_invalid_records > 0:
sys.stderr.write(f'Warning: {self._path} invalid records (value of 9999) in direct horizontal radiation\n')
if cte.HOUR not in building.beam:
building.beam[cte.HOUR] = new_value
else:
pd.concat([building.beam[cte.HOUR], new_value], axis=1)
new_value = wh().cold_water_temperature(building.external_temperature[cte.HOUR]['epw'])
if cte.HOUR not in building.cold_water_temperature:
building.cold_water_temperature[cte.HOUR] = new_value
else:
pd.concat([building.cold_water_temperature[cte.HOUR], new_value], axis=1)
# create the monthly and yearly values out of the hourly
for building in self._city.buildings:
if cte.MONTH not in building.external_temperature:
building.external_temperature[cte.MONTH] = \
wh().get_monthly_mean_values(building.external_temperature[cte.HOUR][['epw']])
if cte.YEAR not in building.external_temperature:
building.external_temperature[cte.YEAR] = \
wh(). get_yearly_mean_values(building.external_temperature[cte.HOUR][['epw']])
if cte.MONTH not in building.cold_water_temperature:
building.cold_water_temperature[cte.MONTH] = wh().get_monthly_mean_values(
building.cold_water_temperature[cte.HOUR][['epw']])
if cte.YEAR not in building.cold_water_temperature:
building.cold_water_temperature[cte.YEAR] = wh().get_yearly_mean_values(
building.cold_water_temperature[cte.HOUR][['epw']])
building.external_temperature[cte.MONTH] = MonthlyValues().get_mean_values(building.external_temperature[cte.HOUR])
building.external_temperature[cte.YEAR] = [sum(building.external_temperature[cte.HOUR]) / 9870]
building.cold_water_temperature[cte.MONTH] = MonthlyValues().get_mean_values(building.cold_water_temperature[cte.HOUR])
building.cold_water_temperature[cte.YEAR] = [sum(building.cold_water_temperature[cte.HOUR]) / 9870]
# If the usage has already being imported, the domestic hot water missing values must be calculated here that
# the cold water temperature is finally known
cold_temperature = building.cold_water_temperature[cte.YEAR]['epw']
cold_temperature = building.cold_water_temperature[cte.YEAR][0]
for internal_zone in building.internal_zones:
if internal_zone.usages is not None:
for usage in internal_zone.usages:

54
hub/imports/weather/helpers/weather.py
View File

@ -4,11 +4,9 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import logging
import math
import calendar as cal
import pandas as pd
import numpy as np
import hub.helpers.constants as cte
@ -75,55 +73,7 @@ class Weather:
for temperature in ambient_temperature_fahrenheit:
radians = (0.986 * (temperature-15-lag) - 90) * math.pi / 180
cold_temperature.append((average_temperature + 6 + ratio * (delta_temperature/2) * math.sin(radians) - 32) * 5/9)
return pd.DataFrame(cold_temperature, columns=['epw'])
def get_monthly_mean_values(self, values):
"""
Get the monthly mean for the given values
:return: float
"""
out = None
if values is not None:
if 'month' not in values.columns:
values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
out = values.groupby('month', as_index=False).mean()
del out['month']
return out
@staticmethod
def get_yearly_mean_values(values):
"""
Get the yearly mean for the given values
:return: float
"""
return values.mean()
def get_total_month(self, values):
"""
Get the total value the given values
:return: float
"""
out = None
if values is not None:
if 'month' not in values.columns:
values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
out = pd.DataFrame(values).groupby('month', as_index=False).sum()
del out['month']
return out
@property
def month_hour(self):
"""
returns a DataFrame that has x values of the month number (January = 1, February = 2...),
being x the number of hours of the corresponding month
:return: DataFrame(int)
"""
array = []
for i in range(0, 12):
days_of_month = cal.monthrange(2015, i+1)[1]
total_hours = days_of_month * 24
array = np.concatenate((array, np.full(total_hours, i + 1)))
return pd.DataFrame(array, columns=['month'])
return cold_temperature
def epw_file(self, region_code):
"""

1
hub/imports/weather_factory.py
View File

@ -4,7 +4,6 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
from pathlib import Path
from hub.city_model_structure.city import City
from hub.helpers.utils import validate_import_export_type

15
tests/test_city_merge.py
View File

@ -4,14 +4,13 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Guille Gutierrez Guillermo.GutierrezMorote@concordia.ca
"""
import copy
import distutils.spawn
import subprocess
from pathlib import Path
from unittest import TestCase
import pandas as pd
from hub.city_model_structure.city import City
from hub.imports.geometry_factory import GeometryFactory
from hub.imports.results_factory import ResultFactory
@ -72,13 +71,13 @@ class TestCityMerge(TestCase):
for building in merged_city.buildings:
for surface in building.surfaces:
if surface.global_irradiance:
full_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
full_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
merged_city_building_total_radiation = 0
for building in merged_city.buildings:
for surface in building.surfaces:
if surface.global_irradiance:
merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
self.assertEqual(full_city_building_total_radiation, merged_city_building_total_radiation)
merged_city = even_city.merge(full_city)
@ -86,24 +85,24 @@ class TestCityMerge(TestCase):
for building in merged_city.buildings:
for surface in building.surfaces:
if surface.global_irradiance:
merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
self.assertEqual(full_city_building_total_radiation, merged_city_building_total_radiation)
for building in even_city.buildings:
for surface in building.surfaces:
surface.global_irradiance[cte.YEAR] = pd.DataFrame([3], columns=['sra_mockup_value'])
surface.global_irradiance[cte.YEAR] = [3]
merged_city = full_city.merge(even_city)
first_merged_city_building_total_radiation = 0
for building in merged_city.buildings:
for surface in building.surfaces:
if surface.global_irradiance:
first_merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
first_merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
merged_city = even_city.merge(full_city)
second_merged_city_building_total_radiation = 0
for building in merged_city.buildings:
for surface in building.surfaces:
if surface.global_irradiance:
second_merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR].iloc[0, 0]
second_merged_city_building_total_radiation += surface.global_irradiance[cte.YEAR][0]
self.assertEqual(first_merged_city_building_total_radiation, second_merged_city_building_total_radiation)

47
tests/test_custom_insel_block.py
View File

@ -37,24 +37,6 @@ class TestExports(TestCase):
self.assertIsNotNone(self._city, 'city is none')
return self._city
@property
def _read_sra_file(self) -> []:
path = (self._example_path / "one_building_in_kelowna_sra_SW.out").resolve()
_results = pd.read_csv(path, sep='\s+', header=0)
id_building = ''
header_building = []
_radiation = []
for column in _results.columns.values:
if id_building != column.split(':')[1]:
id_building = column.split(':')[1]
if len(header_building) > 0:
_radiation.append(pd.concat([MonthlyValues().month_hour, _results[header_building]], axis=1))
header_building = [column]
else:
header_building.append(column)
_radiation.append(pd.concat([MonthlyValues().month_hour, _results[header_building]], axis=1))
return _radiation
def _set_irradiance_surfaces(self, city):
"""
saves in building surfaces the correspondent irradiance at different time-scales depending on the mode
@ -64,19 +46,22 @@ class TestExports(TestCase):
:return: none
"""
city.level_of_detail.surface_radiation = 2
for radiation in self._read_sra_file:
city_object_name = radiation.columns.values.tolist()[1].split(':')[1]
building = city.city_object(city_object_name)
for column in radiation.columns.values:
if column == cte.MONTH:
continue
header_id = column
surface_id = header_id.split(':')[2]
path = (self._example_path / "one_building_in_kelowna_sra_SW.out").resolve()
self._results = pd.read_csv(path, sep='\s+', header=0).to_dict(orient='list')
_irradiance = {}
for key in self._results:
header_name = key.split(':')
result = self._results[key]
city_object_name = header_name[1]
building = self._city.city_object(city_object_name)
surface_id = header_name[2]
surface = building.surface_by_id(surface_id)
new_value = pd.DataFrame(radiation[[header_id]].to_numpy(), columns=['sra'])
surface.global_irradiance[cte.HOUR] = new_value
month_new_value = MonthlyValues().get_mean_values(new_value)
surface.global_irradiance[cte.MONTH] = month_new_value
monthly_result = MonthlyValues.get_total_month(result)
yearly_result = [sum(result)]
_irradiance[cte.YEAR] = yearly_result
_irradiance[cte.MONTH] = monthly_result
_irradiance[cte.HOUR] = result
surface.global_irradiance = _irradiance
def test_insel_monthly_energy_balance_export(self):
"""
@ -86,7 +71,7 @@ class TestExports(TestCase):
WeatherFactory('epw', city).enrich()
for building in city.buildings:
building.external_temperature[cte.MONTH] = MonthlyValues().\
get_mean_values(building.external_temperature[cte.HOUR][['epw']])
get_mean_values(building.external_temperature[cte.HOUR])
self._set_irradiance_surfaces(city)
for building in city.buildings:

20
tests/test_db_factory.py
View File

@ -225,16 +225,16 @@ TestDBFactory
yearly_lighting_peak_load = building.lighting_peak_load[cte.YEAR]
monthly_appliances_peak_load = building.appliances_peak_load[cte.MONTH]
yearly_appliances_peak_load = building.appliances_peak_load[cte.YEAR]
monthly_cooling_demand = building.cooling_demand[cte.MONTH][cte.INSEL_MEB]
yearly_cooling_demand = building.cooling_demand[cte.YEAR][cte.INSEL_MEB]
monthly_heating_demand = building.heating_demand[cte.MONTH][cte.INSEL_MEB]
yearly_heating_demand = building.heating_demand[cte.YEAR][cte.INSEL_MEB]
monthly_lighting_electrical_demand = building.lighting_electrical_demand[cte.MONTH][cte.INSEL_MEB]
yearly_lighting_electrical_demand = building.lighting_electrical_demand[cte.YEAR][cte.INSEL_MEB]
monthly_appliances_electrical_demand = building.appliances_electrical_demand[cte.MONTH][cte.INSEL_MEB]
yearly_appliances_electrical_demand = building.appliances_electrical_demand[cte.YEAR][cte.INSEL_MEB]
monthly_domestic_hot_water_heat_demand = building.domestic_hot_water_heat_demand[cte.MONTH][cte.INSEL_MEB]
yearly_domestic_hot_water_heat_demand = building.domestic_hot_water_heat_demand[cte.YEAR][cte.INSEL_MEB]
monthly_cooling_demand = building.cooling_demand[cte.MONTH]
yearly_cooling_demand = building.cooling_demand[cte.YEAR]
monthly_heating_demand = building.heating_demand[cte.MONTH]
yearly_heating_demand = building.heating_demand[cte.YEAR]
monthly_lighting_electrical_demand = building.lighting_electrical_demand[cte.MONTH]
yearly_lighting_electrical_demand = building.lighting_electrical_demand[cte.YEAR]
monthly_appliances_electrical_demand = building.appliances_electrical_demand[cte.MONTH]
yearly_appliances_electrical_demand = building.appliances_electrical_demand[cte.YEAR]
monthly_domestic_hot_water_heat_demand = building.domestic_hot_water_heat_demand[cte.MONTH]
yearly_domestic_hot_water_heat_demand = building.domestic_hot_water_heat_demand[cte.YEAR]
monthly_heating_consumption = building.heating_consumption[cte.MONTH]
yearly_heating_consumption = building.heating_consumption[cte.YEAR]
monthly_cooling_consumption = building.cooling_consumption[cte.MONTH]

74
tests/test_energy_systems_air_source_hp.py
View File

@ -1,74 +0,0 @@
"""
Test EnergySystemsFactory and various heatpump models
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Peter Yefi peteryefi@gmail.com
"""
from pathlib import Path
import pandas as pd
from unittest import TestCase
from hub.imports.geometry_factory import GeometryFactory
from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.city_model_structure.energy_systems.air_source_hp import AirSourceHP
from hub.exports.energy_systems_factory import EnergySystemsExportFactory
import os
# User defined parameters
user_input = {
'StartYear': 2020,
'EndYear': 2021,
'MaximumHPEnergyInput': 8000,
'HoursOfStorageAtMaxDemand': 1,
'BuildingSuppTemp': 40,
'TemperatureDifference': 15,
'FuelLHV': 47100,
'FuelPrice': 0.12,
'FuelEF': 1887,
'FuelDensity': 0.717,
'HPSupTemp': 60
}
class TestEnergySystemsFactory(TestCase):
"""
TestBuilding TestCase 1
"""
def setUp(self) -> None:
"""
Test setup
:return: None
"""
self._example_path = (Path(__file__).parent / 'tests_data').resolve()
self._output_path = (Path(__file__).parent / 'tests_outputs').resolve()
city_file = (self._example_path/"C40_Final.gml").resolve()
self._output_path = (self._output_path/"as_user_output.csv").resolve()
self._city = GeometryFactory('citygml', path=city_file).city
EnergySystemsFactory('air_source_hp', self._city).enrich()
def test_air_source_heat_pump_import(self):
self.assertIsNotNone(self._city.energy_systems, 'City has energy systems')
self.assertIsInstance(self._city.energy_systems[0].air_source_hp, AirSourceHP)
self.assertEqual(self._city.energy_systems[0].air_source_hp.model, '012')
self.assertEqual(self._city.energy_systems[16].air_source_hp.model, '140')
def test_air_source_series_heat_pump_export(self):
EnergySystemsExportFactory(city=self._city, handler=user_input, hp_model='012',
output_path=self._output_path).export()
df = pd.read_csv(self._output_path)
self.assertEqual(df.shape, (13, 3))
self.assertEqual(df.iloc[0, 1], 1867715.88)
def test_air_source_parallel_heat_pump_export(self):
output = EnergySystemsExportFactory(city=self._city, handler=user_input, hp_model='018',
output_path=None, sim_type=1).export()
self.assertEqual(output["hourly_electricity_demand"][0], 38748.5625)
self.assertIsNotNone(output["daily_fossil_consumption"])
self.assertEqual(len(output["hourly_electricity_demand"]), 8760)
def tearDown(self) -> None:
try:
os.remove(self._output_path)
except OSError:
pass

72
tests/test_energy_systems_water_to_water_hp.py
View File

@ -1,72 +0,0 @@
"""
Test EnergySystemsFactory and various heatpump models
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Peter Yefi peteryefi@gmail.com
"""
from pathlib import Path
from unittest import TestCase
import pandas as pd
from hub.city_model_structure.energy_systems.water_to_water_hp import WaterToWaterHP
from hub.exports.energy_systems_factory import EnergySystemsExportFactory
from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.imports.geometry_factory import GeometryFactory
class TestEnergySystemsFactory(TestCase):
"""
TestEnergySystemsFactory for Water to Water HP
"""
def setUp(self) -> None:
"""
Test setup
:return: None
"""
self._example_path = (Path(__file__).parent / 'tests_data').resolve()
self._output_path = (Path(__file__).parent / 'tests_outputs').resolve()
city_file = (self._example_path / "C40_Final.gml").resolve()
self._output_path = (self._example_path / "w2w_user_output.csv").resolve()
self._city = GeometryFactory('citygml', path=city_file).city
EnergySystemsFactory('water_to_water_hp', self._city).enrich()
def test_water_to_water_heat_pump_import(self):
self.assertIsNotNone(self._city.energy_systems, 'City has energy systems')
self.assertIsInstance(self._city.energy_systems[0].water_to_water_hp, WaterToWaterHP)
self.assertEqual(self._city.energy_systems[0].water_to_water_hp.model, 'ClimateMaster 156 kW')
self.assertEqual(self._city.energy_systems[2].water_to_water_hp.model, 'ClimateMaster 335 kW')
def test_water_to_water_heat_pump_export(self):
# User defined parameters
user_input = {
'StartYear': 2020,
'EndYear': 2021,
'MaximumHPEnergyInput': 8000,
'HoursOfStorageAtMaxDemand': 1,
'BuildingSuppTemp': 40,
'TemperatureDifference': 15,
'FuelLHV': 47100,
'FuelPrice': 0.12,
'FuelEF': 1887,
'FuelDensity': 0.717,
'HPSupTemp': 60,
'b1': 10,
'b2': 10,
'b3': 10,
'b4': 10,
'b5': 10,
'b6': 10,
'b7': 10,
'b8': 10,
'b9': 10,
'b10': 10,
'b11': 10
}
EnergySystemsExportFactory(city=self._city, handler=user_input, hp_model='ClimateMaster 256 kW',
output_path=self._output_path, sim_type=1).export('water')
df = pd.read_csv(self._output_path)
self.assertEqual(df.shape, (13, 3))
self.assertEqual(df.iloc[0, 1], 1031544.62)

10
tests/test_exports.py
View File

@ -5,10 +5,10 @@ Copyright © 2022 Concordia CERC group
Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
Code contributors: Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import logging.handlers
from pathlib import Path
from unittest import TestCase
import pandas as pd
from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
@ -58,10 +58,10 @@ class TestExports(TestCase):
self._complete_city.climate_reference_city = 'Summerland'
dummy_measures = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for building in self._complete_city.buildings:
building.heating_demand[cte.MONTH] = pd.DataFrame({'INSEL': dummy_measures})
building.cooling_demand[cte.MONTH] = pd.DataFrame({'INSEL': dummy_measures})
building.heating_demand[cte.YEAR] = pd.DataFrame({'INSEL': [0.0]})
building.cooling_demand[cte.YEAR] = pd.DataFrame({'INSEL': [0.0]})
building.heating_demand[cte.MONTH] = dummy_measures
building.cooling_demand[cte.MONTH] = dummy_measures
building.heating_demand[cte.YEAR] = [0.0]
building.cooling_demand[cte.YEAR] = [0.0]
return self._complete_city
def _export(self, export_type, from_pickle=False):

76
tests/test_heat_pump_results.py
View File

@ -1,76 +0,0 @@
"""
Test EnergySystemsFactory and various heatpump models
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2023 Concordia CERC group
Project Coder Peter Yefi peteryefi@gmail.com
"""
from pathlib import Path
from unittest import TestCase
from hub.imports.geometry_factory import GeometryFactory
from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.exports.energy_systems_factory import EnergySystemsExportFactory
from hub.imports.results_factory import ResultFactory
import os
from pandas.core.series import Series
# User defined parameters
user_input = {
'StartYear': 2020,
'EndYear': 2021,
'MaximumHPEnergyInput': 8000,
'HoursOfStorageAtMaxDemand': 1,
'BuildingSuppTemp': 40,
'TemperatureDifference': 15,
'FuelLHV': 47100,
'FuelPrice': 0.12,
'FuelEF': 1887,
'FuelDensity': 0.717,
'HPSupTemp': 60
}
class TestHeatPumpResults(TestCase):
"""
TestHeatPumpResults
"""
def setUp(self) -> None:
"""
Test setup
:return: None
"""
self._example_path = (Path(__file__).parent / 'tests_data').resolve()
self._output_path = (Path(__file__).parent / 'tests_outputs/as_user_output.csv').resolve()
city_file = (self._example_path / "C40_Final.gml").resolve()
self._city = GeometryFactory('citygml', path=city_file).city
EnergySystemsFactory('air_source_hp', self._city).enrich()
def test_air_source_series_heat_pump_012_results(self):
EnergySystemsExportFactory(city=self._city, handler=user_input, hp_model='012',
output_path=self._output_path).export()
ResultFactory('heat_pump', self._city, self._output_path, '012').enrich()
for energy_system in self._city.energy_systems:
self.assertIsNone(energy_system.water_to_water_hp)
if energy_system.air_source_hp.model == '012':
self.assertIsInstance(energy_system.air_source_hp.hp_monthly_fossil_consumption, Series)
self.assertEqual(energy_system.air_source_hp.hp_monthly_fossil_consumption.iloc[5], 1.51325583)
self.assertEqual(energy_system.air_source_hp.hp_monthly_fossil_consumption.iloc[12], 35.853598782915)
def test_air_source_series_heat_pump_015_results(self):
EnergySystemsExportFactory(city=self._city, handler=user_input, hp_model='140',
output_path=self._output_path).export()
ResultFactory('heat_pump', self._city, self._output_path, '140').enrich()
for energy_system in self._city.energy_systems:
self.assertIsNone(energy_system.water_to_water_hp)
if energy_system.air_source_hp.model == '140':
self.assertIsInstance(energy_system.air_source_hp.hp_monthly_fossil_consumption, Series)
self.assertEqual(energy_system.air_source_hp.hp_monthly_fossil_consumption.iloc[0], 7.91282225)
self.assertEqual(energy_system.air_source_hp.hp_monthly_fossil_consumption.iloc[2], 0.068873927)
def tearDown(self) -> None:
try:
os.remove(self._output_path)
except OSError:
pass

47
tests/test_insel_exports.py
View File

@ -37,24 +37,6 @@ class TestExports(TestCase):
self.assertIsNotNone(self._city, 'city is none')
return self._city
@property
def _read_sra_file(self) -> []:
path = (self._example_path / "one_building_in_kelowna_sra_SW.out").resolve()
_results = pd.read_csv(path, sep='\s+', header=0)
id_building = ''
header_building = []
_radiation = []
for column in _results.columns.values:
if id_building != column.split(':')[1]:
id_building = column.split(':')[1]
if len(header_building) > 0:
_radiation.append(pd.concat([MonthlyValues().month_hour, _results[header_building]], axis=1))
header_building = [column]
else:
header_building.append(column)
_radiation.append(pd.concat([MonthlyValues().month_hour, _results[header_building]], axis=1))
return _radiation
def _set_irradiance_surfaces(self, city):
"""
saves in building surfaces the correspondent irradiance at different time-scales depending on the mode
@ -64,19 +46,22 @@ class TestExports(TestCase):
:return: none
"""
city.level_of_detail.surface_radiation = 2
for radiation in self._read_sra_file:
city_object_name = radiation.columns.values.tolist()[1].split(':')[1]
building = city.city_object(city_object_name)
for column in radiation.columns.values:
if column == cte.MONTH:
continue
header_id = column
surface_id = header_id.split(':')[2]
path = (self._example_path / "one_building_in_kelowna_sra_SW.out").resolve()
self._results = pd.read_csv(path, sep='\s+', header=0).to_dict(orient='list')
_irradiance = {}
for key in self._results:
header_name = key.split(':')
result = self._results[key]
city_object_name = header_name[1]
building = self._city.city_object(city_object_name)
surface_id = header_name[2]
surface = building.surface_by_id(surface_id)
new_value = pd.DataFrame(radiation[[header_id]].to_numpy(), columns=['sra'])
surface.global_irradiance[cte.HOUR] = new_value
month_new_value = MonthlyValues().get_mean_values(new_value)
surface.global_irradiance[cte.MONTH] = month_new_value
monthly_result = MonthlyValues.get_total_month(result)
yearly_result = [sum(result)]
_irradiance[cte.YEAR] = yearly_result
_irradiance[cte.MONTH] = monthly_result
_irradiance[cte.HOUR] = result
surface.global_irradiance = _irradiance
def test_insel_monthly_energy_balance_export(self):
"""
@ -86,7 +71,7 @@ class TestExports(TestCase):
WeatherFactory('epw', city).enrich()
for building in city.buildings:
building.external_temperature[cte.MONTH] = MonthlyValues().\
get_mean_values(building.external_temperature[cte.HOUR][['epw']])
get_mean_values(building.external_temperature[cte.HOUR])
self._set_irradiance_surfaces(city)
for building in city.buildings:

15
tests/test_results_import.py
View File

@ -4,12 +4,11 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
"""
import subprocess
from pathlib import Path
from unittest import TestCase
import pandas as pd
import hub.helpers.constants as cte
from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
from hub.exports.exports_factory import ExportsFactory
@ -65,10 +64,10 @@ class TestResultsImport(TestCase):
ResultFactory('insel_monthly_energy_balance', self._city, self._output_path).enrich()
# Check that all the buildings have heating and cooling values
for building in self._city.buildings:
self.assertIsNotNone(building.heating_demand[cte.MONTH][cte.INSEL_MEB])
self.assertIsNotNone(building.cooling_demand[cte.MONTH][cte.INSEL_MEB])
self.assertIsNotNone(building.heating_demand[cte.YEAR][cte.INSEL_MEB])
self.assertIsNotNone(building.cooling_demand[cte.YEAR][cte.INSEL_MEB])
self.assertIsNotNone(building.heating_demand[cte.MONTH])
self.assertIsNotNone(building.cooling_demand[cte.MONTH])
self.assertIsNotNone(building.heating_demand[cte.YEAR])
self.assertIsNotNone(building.cooling_demand[cte.YEAR])
self.assertIsNotNone(building.lighting_peak_load[cte.MONTH])
self.assertIsNotNone(building.lighting_peak_load[cte.YEAR])
self.assertIsNotNone(building.appliances_peak_load[cte.MONTH])
@ -89,7 +88,7 @@ class TestResultsImport(TestCase):
expected_monthly_list = [0 for _ in range(12)]
expected_monthly_list[0] = 1000
for building in self._city.buildings:
building.heating_demand[cte.HOUR] = pd.DataFrame(values, columns=['dummy'])
building.cooling_demand[cte.HOUR] = pd.DataFrame(values, columns=['dummy'])
building.heating_demand[cte.HOUR] = values
building.cooling_demand[cte.HOUR] = values
self.assertIsNotNone(building.heating_peak_load)
self.assertIsNotNone(building.cooling_peak_load)