119 lines
6.4 KiB
Python
119 lines
6.4 KiB
Python
import datetime
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import psycopg2
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from pymongo import MongoClient
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from co2_emission.co2_emission import Co2Emission
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from costs.cost import Cost
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from building import Building
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from city_object import CityObject
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from hub.catalog_factories.energy_systems_catalog_factory import EnergySystemsCatalogFactory
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def calculate_building(building_info, building_results, scenario_id):
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energy_systems_catalog = EnergySystemsCatalogFactory('montreal_custom').catalog
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archetype = energy_systems_catalog.get_entry(building_info.system_name)
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mockup_building = Building(building_info, building_results, archetype)
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life_cycle = Cost(mockup_building, retrofit_scenario=scenario_id).life_cycle
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operational_co2 = Co2Emission(mockup_building).operational_co2
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global_capital_costs = life_cycle[f'Scenario {scenario_id}']['global_capital_costs']
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global_operational_costs = life_cycle[f'Scenario {scenario_id}']['global_operational_costs']
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global_capital_incomes = life_cycle[f'Scenario {scenario_id}']['global_capital_incomes']
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global_maintenance_costs = life_cycle[f'Scenario {scenario_id}']['global_maintenance_costs']
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building_results['total_heating_area'] = building_info.total_heating_area
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building_results['year_of_construction'] = building_info.year_of_construction
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building_results['function'] = building_info.function
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building_results['costs'] = {
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'total_capital_costs_skin': life_cycle[f'Scenario {scenario_id}']['total_capital_costs_skin'],
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'total_capital_costs_systems': life_cycle[f'Scenario {scenario_id}']['total_capital_costs_systems'],
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'end_of_life_costs': life_cycle[f'Scenario {scenario_id}']['end_of_life_costs'],
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'total_operational_costs': life_cycle[f'Scenario {scenario_id}']['total_operational_costs'],
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'total_maintenance_costs': life_cycle[f'Scenario {scenario_id}']['total_maintenance_costs'],
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'operational_incomes': life_cycle[f'Scenario {scenario_id}']['operational_incomes'],
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'capital_incomes': life_cycle[f'Scenario {scenario_id}']['capital_incomes'],
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'global_capital_costs': {
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'B2010_opaque_walls': global_capital_costs['B2010_opaque_walls'].tolist(),
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'B2020_transparent': global_capital_costs['B2020_transparent'].tolist(),
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'B3010_opaque_roof': global_capital_costs['B3010_opaque_roof'].tolist(),
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'B10_superstructure': global_capital_costs['B10_superstructure'].tolist(),
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'D3020_heat_generating_systems': global_capital_costs['D3020_heat_generating_systems'].tolist(),
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'D3030_cooling_generation_systems': global_capital_costs['D3030_cooling_generation_systems'].tolist(),
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'D3080_other_hvac_ahu': global_capital_costs['D3080_other_hvac_ahu'].tolist(),
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'D5020_lighting_and_branch_wiring': global_capital_costs['D5020_lighting_and_branch_wiring'].tolist(),
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'D301010_photovoltaic_system': global_capital_costs['D301010_photovoltaic_system'].tolist(),
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},
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'global_end_of_life_costs': life_cycle[f'Scenario {scenario_id}']['global_end_of_life_costs'][
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'End_of_life_costs'].tolist(),
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'global_operational_costs': {
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'fixed_costs_electricity_peak': global_operational_costs['Fixed_costs_electricity_peak'].tolist(),
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'fixed_costs_electricity_monthly': global_operational_costs['Fixed_costs_electricity_monthly'].tolist(),
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'variable_costs_electricity': global_operational_costs['Variable_costs_electricity'].tolist(),
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'fixed_costs_gas': global_operational_costs['Fixed_costs_gas'].tolist(),
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'variable_costs_gas': global_operational_costs['Variable_costs_gas'].tolist()
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},
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'global_maintenance_costs': {
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'heating_maintenance': global_maintenance_costs['Heating_maintenance'].tolist(),
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'cooling_maintenance': global_maintenance_costs['Cooling_maintenance'].tolist(),
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'pv_maintenance': global_maintenance_costs['PV_maintenance'].tolist(),
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},
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'global_operational_incomes': life_cycle[f'Scenario {scenario_id}']['global_operational_incomes'][
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'Incomes electricity'].tolist(),
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'global_capital_incomes': {
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'subsidies_construction': global_capital_incomes['Subsidies construction'].tolist(),
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'subsidies_hvac': global_capital_incomes['Subsidies HVAC'].tolist(),
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'subsidies_pv': global_capital_incomes['Subsidies PV'].tolist()
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}
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}
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building_results['operational_co2'] = operational_co2
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start = datetime.datetime.now()
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scenario_ids = {'current status': 0,
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'skin retrofit': 1,
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'system retrofit and pv': 2,
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'skin and system retrofit with pv': 3
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}
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connection = psycopg2.connect(database='montreal_retrofit', user='retrofit', password='C3rcIT$vc#', host='127.0.0.1',
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port=5432)
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cursor = connection.cursor()
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client = MongoClient('mongodb://localhost:27017/')
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montreal_retrofit_db = client.montreal_retrofit
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meb_collection = montreal_retrofit_db.me
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cursor.execute("SELECT id, scenario FROM city;")
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cities = cursor.fetchall()
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for city in cities:
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cursor.execute(
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f'SELECT DISTINCT id, name, aliases, year_of_construction, function, usage, volume, area, total_heating_area, '
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f'wall_area, windows_area, roof_area, total_pv_area, system_name FROM city_object WHERE city_id = {city[0]}')
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buildings = cursor.fetchall()
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for building in buildings:
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cursor.execute(f'SELECT values FROM simulation_results WHERE city_object_id = {building[0]};')
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values = cursor.fetchall()
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for value in values:
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building_data = {'city_id': city[0], 'name': building[1], 'alias': building[2].split(',')[0][1:],
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'year_of_construction': building[3],
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'function': building[4], 'usage': building[5], 'volume': building[6], 'area': building[7],
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'total_heating_area': building[8], 'wall_area': building[9], 'windows_area': building[10],
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'roof_area': building[11], 'total_pv_area': building[12], 'system_name': building[13],
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'scenario': city[1]}
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city_object = CityObject(building_data)
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results = values[0][0]
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try:
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calculate_building(city_object, results, scenario_ids[city[1]])
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except KeyError:
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pass
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building_data['insel meb'] = results['insel meb']
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building_data['costs'] = results['costs']
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building_data['operational_co2'] = results['operational_co2']
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try:
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montreal_retrofit_db[f'meb_{city[1].replace(" ", "_")}'].insert_one(building_data)
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except:
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pass
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print(f'Completed in: {datetime.datetime.now() - start}')
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