forked from s_ranjbar/city_retrofit
The energy plus result importer for multiple buildings is created and tested. Currently, buildings are enriched with hourly values but monthly and yearly values are not working.
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020190829f
@ -12,89 +12,54 @@ import csv
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import hub.helpers.constants as cte
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class EnergyPlusSingleBuilding:
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class EnergyPlusMultipleBuildings:
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def __init__(self, city, base_path):
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self._city = city
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self._base_path = base_path
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@staticmethod
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def _building_energy_demands(energy_plus_output_file_path):
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def _building_energy_demands(self, energy_plus_output_file_path):
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buildings_energy_demands = {}
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with open(Path(energy_plus_output_file_path).resolve(), 'r', encoding='utf8') as csv_file:
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csv_output = csv.reader(csv_file)
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headers = next(csv_output)
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building_energy_demands = {
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'Heating (J)': [],
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'Cooling (J)': [],
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'DHW (J)': [],
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'Appliances (J)': [],
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'Lighting (J)': []
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}
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heating_column_index = []
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cooling_column_index = []
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dhw_column_index = []
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appliance_column_index = []
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lighting_column_index = []
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for index, header in enumerate(headers):
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if "Total Heating" in header:
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heating_column_index.append(index)
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elif "Total Cooling" in header:
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cooling_column_index.append(index)
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elif "DHW" in header:
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dhw_column_index.append(index)
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elif "InteriorEquipment" in header:
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appliance_column_index.append(index)
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elif "InteriorLights" in header:
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lighting_column_index.append(index)
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csv_output = list(csv.DictReader(csv_file))
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for line in csv_output:
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total_heating_demand = 0
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total_cooling_demand = 0
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total_dhw_demand = 0
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total_appliance_demand = 0
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total_lighting_demand = 0
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for heating_index in heating_column_index:
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total_heating_demand += float(line[heating_index])
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building_energy_demands['Heating (J)'].append(total_heating_demand)
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for cooling_index in cooling_column_index:
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total_cooling_demand += float(line[cooling_index])
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building_energy_demands['Cooling (J)'].append(total_cooling_demand)
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for dhw_index in dhw_column_index:
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total_dhw_demand += float(line[dhw_index]) * cte.WATTS_HOUR_TO_JULES
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building_energy_demands['DHW (J)'].append(total_dhw_demand)
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for appliance_index in appliance_column_index:
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total_appliance_demand += float(line[appliance_index])
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building_energy_demands['Appliances (J)'].append(total_appliance_demand)
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for lighting_index in lighting_column_index:
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total_lighting_demand += float(line[lighting_index])
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building_energy_demands['Lighting (J)'].append(total_lighting_demand)
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return building_energy_demands
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for building in self._city.buildings:
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building_name = building.name
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buildings_energy_demands[f'Building {building_name} Heating Demand (J)'] = [
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float(
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row[f"{building_name} IDEAL LOADS AIR SYSTEM:Zone Ideal Loads Supply Air Total Heating Energy [J](Hourly)"])
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for row in csv_output
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]
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buildings_energy_demands[f'Building {building_name} Cooling Demand (J)'] = [
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float(
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row[f"{building_name} IDEAL LOADS AIR SYSTEM:Zone Ideal Loads Supply Air Total Cooling Energy [J](Hourly)"])
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for row in csv_output
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]
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buildings_energy_demands[f'Building {building_name} DHW Demand (W)'] = [
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float(row[f"DHW {building.name}:Water Use Equipment Heating Rate [W](Hourly)"])
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for row in csv_output
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]
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buildings_energy_demands[f'Building {building_name} Appliances (W)'] = [
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float(row[f"{building_name}_APPLIANCE:Other Equipment Electricity Rate [W](Hourly)"])
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for row in csv_output
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]
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buildings_energy_demands[f'Building {building_name} Lighting (W)'] = [
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float(row[f"{building_name}:Zone Lights Electricity Rate [W](Hourly)"]) for row in csv_output
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]
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return buildings_energy_demands
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def enrich(self):
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"""
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Enrich the city by using the energy plus workflow output files (J)
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:return: None
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"""
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for building in self._city.buildings:
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file_name = f'{building.name}_out.csv'
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energy_plus_output_file_path = Path(self._base_path / file_name).resolve()
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if energy_plus_output_file_path.is_file():
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building_energy_demands = self._building_energy_demands(energy_plus_output_file_path)
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building.heating_demand[cte.HOUR] = building_energy_demands['Heating (J)']
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building.cooling_demand[cte.HOUR] = building_energy_demands['Cooling (J)']
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building.domestic_hot_water_heat_demand[cte.HOUR] = building_energy_demands['DHW (J)']
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building.appliances_electrical_demand[cte.HOUR] = building_energy_demands['Appliances (J)']
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building.lighting_electrical_demand[cte.HOUR] = building_energy_demands['Lighting (J)']
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building.heating_demand[cte.MONTH] = MonthlyValues.get_total_month(building.heating_demand[cte.HOUR])
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building.cooling_demand[cte.MONTH] = MonthlyValues.get_total_month(building.cooling_demand[cte.HOUR])
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building.domestic_hot_water_heat_demand[cte.MONTH] = (
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MonthlyValues.get_total_month(building.domestic_hot_water_heat_demand[cte.HOUR]))
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building.appliances_electrical_demand[cte.MONTH] = (
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MonthlyValues.get_total_month(building.appliances_electrical_demand[cte.HOUR]))
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building.lighting_electrical_demand[cte.MONTH] = (
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MonthlyValues.get_total_month(building.lighting_electrical_demand[cte.HOUR]))
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building.heating_demand[cte.YEAR] = [sum(building.heating_demand[cte.MONTH])]
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building.cooling_demand[cte.YEAR] = [sum(building.cooling_demand[cte.MONTH])]
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building.domestic_hot_water_heat_demand[cte.YEAR] = [sum(building.domestic_hot_water_heat_demand[cte.MONTH])]
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building.appliances_electrical_demand[cte.YEAR] = [sum(building.appliances_electrical_demand[cte.MONTH])]
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building.lighting_electrical_demand[cte.YEAR] = [sum(building.lighting_electrical_demand[cte.MONTH])]
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file_name = f'{self._city.name}_out.csv'
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energy_plus_output_file_path = Path(self._base_path / file_name).resolve()
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if energy_plus_output_file_path.is_file():
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building_energy_demands = self._building_energy_demands(energy_plus_output_file_path)
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for building in self._city.buildings:
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building.heating_demand[cte.HOUR] = building_energy_demands[f'Building {building.name} Heating Demand (J)']
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building.cooling_demand[cte.HOUR] = building_energy_demands[f'Building {building.name} Cooling Demand (J)']
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building.domestic_hot_water_heat_demand[cte.HOUR] = building_energy_demands[f'Building {building.name} DHW Demand (W)']
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building.appliances_electrical_demand[cte.HOUR] = building_energy_demands[f'Building {building.name} Appliances (W)']
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@ -10,8 +10,8 @@ from pathlib import Path
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from hub.helpers.utils import validate_import_export_type
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from hub.imports.results.insel_monthly_energry_balance import InselMonthlyEnergyBalance
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from hub.imports.results.simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
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from hub.imports.results.energy_plus import EnergyPlus
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# from hub.imports.results.ep_multiple_buildings import EnergyPlusMultiple
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from hub.imports.results.ep_multiple_buildings import EnergyPlusMultipleBuildings
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from hub.imports.results.energy_plus_single_building import EnergyPlusSingleBuilding
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class ResultFactory:
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@ -54,11 +54,11 @@ class ResultFactory:
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"""
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EnergyPlusSingleBuilding(self._city, self._base_path).enrich()
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# def _energy_plus_multiple(self):
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# """
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# Enrich the city with energy plus results
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# """
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# EnergyPlusMultiple(self._city, self._base_path).enrich()
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def _energy_plus_multiple_buildings(self):
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"""
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Enrich the city with energy plus results
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"""
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EnergyPlusMultipleBuildings(self._city, self._base_path).enrich()
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def enrich(self):
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"""
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@ -30,11 +30,11 @@ class TestResultsImport(TestCase):
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"""
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self._example_path = (Path(__file__).parent / 'tests_data').resolve()
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self._output_path = (Path(__file__).parent / 'tests_outputs').resolve()
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file = 'test.geojson'
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file = 'Citylayers_neighbours_simp2.json'
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file_path = (self._example_path / file).resolve()
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self._city = GeometryFactory('geojson',
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path=file_path,
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height_field='citygml_me',
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height_field='heightmax',
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year_of_construction_field='ANNEE_CONS',
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function_field='CODE_UTILI',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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@ -115,3 +115,25 @@ class TestResultsImport(TestCase):
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self.assertDictEqual(building.domestic_hot_water_heat_demand, {})
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self.assertDictEqual(building.lighting_electrical_demand, {})
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self.assertDictEqual(building.appliances_electrical_demand, {})
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def test_energy_plus_multiple_buildings_results_import(self):
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ResultFactory('energy_plus_multiple_buildings', self._city, self._example_path).enrich()
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csv_output_name = f'{self._city.name}_out.csv'
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csv_output_path = (self._example_path / csv_output_name).resolve()
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if csv_output_path.is_file():
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for building in self._city.buildings:
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self.assertIsNotNone(building.heating_demand)
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self.assertIsNotNone(building.cooling_demand)
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self.assertIsNotNone(building.domestic_hot_water_heat_demand)
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self.assertIsNotNone(building.lighting_electrical_demand)
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self.assertIsNotNone(building.appliances_electrical_demand)
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total_demand = sum(building.heating_demand[cte.HOUR])
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self.assertAlmostEqual(total_demand, building.heating_demand[cte.YEAR][0], 3)
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total_demand = sum(building.heating_demand[cte.MONTH])
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self.assertEqual(total_demand, building.heating_demand[cte.YEAR][0], 3)
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# if building.name != '12':
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# self.assertDictEqual(building.heating_demand, {})
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# self.assertDictEqual(building.cooling_demand, {})
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# self.assertDictEqual(building.domestic_hot_water_heat_demand, {})
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# self.assertDictEqual(building.lighting_electrical_demand, {})
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# self.assertDictEqual(building.appliances_electrical_demand, {})
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6
tests/tests_data/Citylayers_neighbours_simp2.json
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6
tests/tests_data/Citylayers_neighbours_simp2.json
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@ -0,0 +1,6 @@
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{"type":"FeatureCollection", "features": [
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{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-73.57185265377689,45.52343504199365],[-73.57186901784087,45.52344250726594],[-73.5719186361361,45.523388576721516],[-73.57169557189309,45.52328954697509],[-73.57164717307853,45.523341318247276],[-73.57185265377689,45.52343504199365]]]},"id":86522, "properties":{"ID_UEV":"01027575","CIVIQUE_DE":" 951","CIVIQUE_FI":" 959","NOM_RUE":"rue Napoléon (MTL)","SUITE_DEBU":" ","MUNICIPALI":"50","ETAGE_HORS":3,"NOMBRE_LOG":5,"ANNEE_CONS":1927,"CODE_UTILI":"1000","LETTRE_DEB":" ","LETTRE_FIN":" ","LIBELLE_UT":"Logement","CATEGORIE_":"Régulier","MATRICULE8":"9942-16-9406-0-000-0000","SUPERFICIE":189,"SUPERFIC_1":342,"NO_ARROND_":"REM21","Shape_Leng":0.000632076842566,"OBJECTID":86522,"Join_Count":1,"TARGET_FID":86522,"feature_id":"bd64c84e-a70a-4112-915e-74b79f16c189","md_id":" ","acqtech":1360,"acqtech_en":"Lidar","acqtech_fr":"Lidar","provider":461,"provideren":"Municipal","providerfr":"Municipal","datemin":"20151124","datemax":"20151208","haccmin":2,"haccmax":2,"vaccmin":1,"vaccmax":1,"heightmin":2.03,"heightmax":16.11,"elevmin":43.98,"elevmax":46.16,"bldgarea":2049.47,"comment":"Detection of Lidar points classified as ground in the building. Détection de points Lidar classifiés sol dans le bâtiment.","OBJECTID_1":86522,"Shape_Le_1":0.000632076842566,"Shape_Ar_1":1.66656602227e-8,"OBJECTID_12":86522,"Join_Count_1":4,"TARGET_FID_1":86521,"g_objectid":"965958","g_co_mrc":"66023","g_code_mun":"66023","g_arrond":"REM21","g_anrole":"2019","g_usag_pre":"Résidentiel","g_no_lot":"1885186","g_nb_poly_":"1","g_utilisat":"1000","g_nb_logem":"5","g_nb_locau":" ","g_descript":"Unité d'évaluation","g_id_provi":"66023994216872540000000","g_sup_tota":"228.4","g_geometry":"0.000850333","g_geomet_1":"2.66017e-008","g_dat_acqu":"2020-02-12T00:00:00","g_dat_char":"2020-02-17T00:00:00","Shape_Leng_1":0.000632076842566,"Shape_Area_1":1.66656602227e-8,"Shape_Length":0.0006320773708141846,"Shape_Area":1.66656602227e-8}},
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{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-73.57153144831767,45.523465428286954],[-73.57149617241038,45.523503246577604],[-73.5714876720184,45.52349934711725],[-73.57146469883673,45.52352357665177],[-73.571684848377,45.523623993152796],[-73.5717241721328,45.523584846563494],[-73.57163387210545,45.52354014666048],[-73.57165310140948,45.523520919155146],[-73.57153144831767,45.523465428286954]]]},"id":92980,"properties":{"ID_UEV":"01105693","CIVIQUE_DE":" 981","CIVIQUE_FI":" 981","NOM_RUE":"rue Napoléon (MTL)","SUITE_DEBU":" ","MUNICIPALI":"50","ETAGE_HORS":1,"NOMBRE_LOG":1,"ANNEE_CONS":1927,"CODE_UTILI":"1000","LETTRE_DEB":" ","LETTRE_FIN":" ","LIBELLE_UT":"Logement","CATEGORIE_":"Condominium","MATRICULE8":"9942-26-0726-9-001-0002","SUPERFICIE":64,"SUPERFIC_1":1097,"NO_ARROND_":"REM21","Shape_Leng":0.000653576866621,"OBJECTID":92980,"Join_Count":1,"TARGET_FID":92980,"feature_id":"bd64c84e-a70a-4112-915e-74b79f16c189","md_id":" ","acqtech":1360,"acqtech_en":"Lidar","acqtech_fr":"Lidar","provider":461,"provideren":"Municipal","providerfr":"Municipal","datemin":"20151124","datemax":"20151208","haccmin":2,"haccmax":2,"vaccmin":1,"vaccmax":1,"heightmin":2.03,"heightmax":16.11,"elevmin":43.98,"elevmax":46.16,"bldgarea":2049.47,"comment":"Detection of Lidar points classified as ground in the building. Détection de points Lidar classifiés sol dans le bâtiment.","OBJECTID_1":92980,"Shape_Le_1":0.000653576866621,"Shape_Ar_1":1.64021795264e-8,"OBJECTID_12":92980,"Join_Count_1":3,"TARGET_FID_1":92979,"g_objectid":"967849","g_co_mrc":"66023","g_code_mun":"66023","g_arrond":"REM21","g_anrole":"2019","g_usag_pre":"Résidentiel","g_no_lot":"2316950","g_nb_poly_":"1","g_utilisat":"1000","g_nb_logem":"6","g_nb_locau":" ","g_descript":"Unité d'évaluation","g_id_provi":"66023994226021950000000","g_sup_tota":"205.6","g_geometry":"0.000725388","g_geomet_1":"2.3681e-008","g_dat_acqu":"2020-02-12T00:00:00","g_dat_char":"2020-02-17T00:00:00","Shape_Leng_1":0.000653576866621,"Shape_Area_1":1.64021795264e-8,"Shape_Length":0.0006535770208011338,"Shape_Area":1.64021795264e-8}},
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{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-73.57185265377689,45.52343504199365],[-73.57164717307853,45.523341318247276],[-73.57159495574257,45.523397333420235],[-73.57180091488027,45.52349127660119],[-73.57185265377689,45.52343504199365]]]},"id":146189, "properties":{"ID_UEV":"01027577","CIVIQUE_DE":" 961","CIVIQUE_FI":" 965","NOM_RUE":"rue Napoléon (MTL)","SUITE_DEBU":" ","MUNICIPALI":"50","ETAGE_HORS":3,"NOMBRE_LOG":3,"ANNEE_CONS":1927,"CODE_UTILI":"1000","LETTRE_DEB":" ","LETTRE_FIN":" ","LIBELLE_UT":"Logement","CATEGORIE_":"Régulier","MATRICULE8":"9942-16-9812-9-000-0000","SUPERFICIE":159,"SUPERFIC_1":313,"NO_ARROND_":"REM21","Shape_Leng":0.00060521313674,"OBJECTID":146189,"Join_Count":1,"TARGET_FID":146189,"feature_id":"bd64c84e-a70a-4112-915e-74b79f16c189","md_id":" ","acqtech":1360,"acqtech_en":"Lidar","acqtech_fr":"Lidar","provider":461,"provideren":"Municipal","providerfr":"Municipal","datemin":"20151124","datemax":"20151208","haccmin":2,"haccmax":2,"vaccmin":1,"vaccmax":1,"heightmin":2.03,"heightmax":16.11,"elevmin":43.98,"elevmax":46.16,"bldgarea":2049.47,"comment":"Detection of Lidar points classified as ground in the building. Détection de points Lidar classifiés sol dans le bâtiment.","OBJECTID_1":146189,"Shape_Le_1":0.00060521313674,"Shape_Ar_1":1.64233386484e-8,"OBJECTID_12":146189,"Join_Count_1":4,"TARGET_FID_1":146188,"g_objectid":"967849","g_co_mrc":"66023","g_code_mun":"66023","g_arrond":"REM21","g_anrole":"2019","g_usag_pre":"Résidentiel","g_no_lot":"2316950","g_nb_poly_":"1","g_utilisat":"1000","g_nb_logem":"6","g_nb_locau":" ","g_descript":"Unité d'évaluation","g_id_provi":"66023994226021950000000","g_sup_tota":"205.6","g_geometry":"0.000725388","g_geomet_1":"2.3681e-008","g_dat_acqu":"2020-02-12T00:00:00","g_dat_char":"2020-02-17T00:00:00","Shape_Leng_1":0.00060521313674,"Shape_Area_1":1.64233386484e-8,"Shape_Length":0.0006052125392461572,"Shape_Area":1.64233386484e-8}},
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||||
]}
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