69 lines
3.2 KiB
Python
69 lines
3.2 KiB
Python
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"""
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Costs Workflow
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
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Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
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"""
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from pathlib import Path
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import pandas as pd
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from hub.helpers.dictionaries import Dictionaries
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from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
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from costs import EMISSION_FACTOR_ELECTRICITY_QUEBEC, EMISSION_FACTOR_GAS_QUEBEC, EMISSION_FACTOR_BIOMASS_QUEBEC, \
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EMISSION_FACTOR_FUEL_OIL_QUEBEC, EMISSION_FACTOR_DIESEL_QUEBEC, NUMBER_OF_YEARS
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def _search_archetype(costs_catalog, building_function):
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costs_archetypes = costs_catalog.entries('archetypes').archetypes
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for building_archetype in costs_archetypes:
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if str(building_function) == str(building_archetype.function):
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return building_archetype
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raise KeyError('archetype not found')
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catalog = CostCatalogFactory('montreal_custom').catalog
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for building in city.buildings:
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building_heating_consumption = 1000
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building_domestic_water_consumption = 1000
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building_cooling_consumption = 1000
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distribution_systems_electrical_consumption = 1000
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lighting_electrical_demand = 1000
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appliances_electrical_demand = 1000
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rng = range(NUMBER_OF_YEARS)
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function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
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archetype = _search_archetype(catalog, function)
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print('co2 for first building started')
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if "gas" in building.energy_systems_archetype_name:
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gas_consumption = building_heating_consumption + building_domestic_water_consumption
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electricity_consumption = building_cooling_consumption + distribution_systems_electrical_consumption + \
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lighting_electrical_demand + appliances_electrical_demand
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biomass_consumption = 0
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fuel_oil_consumption = 0
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diesel_consumption = 0
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else:
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gas_consumption = 0
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electricity_consumption = building_heating_consumption + building_domestic_water_consumption + \
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building_cooling_consumption + distribution_systems_electrical_consumption + \
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lighting_electrical_demand + appliances_electrical_demand
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biomass_consumption = 0
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fuel_oil_consumption = 0
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diesel_consumption = 0
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CO2_emissions = pd.DataFrame(index=rng, columns=['CO2 emissions gas', 'CO2 emissions electricity',
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'CO2 Emissions biomass', 'CO2 emissions fueloil',
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'CO2 emissions diesel'], dtype='float')
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for year in range(1, NUMBER_OF_YEARS+1):
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CO2_emissions.at[year,'CO2 emissions gas'] = gas_consumption * EMISSION_FACTOR_GAS_QUEBEC
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CO2_emissions.at[year, 'CO2 emissions electricity'] = electricity_consumption * EMISSION_FACTOR_ELECTRICITY_QUEBEC
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CO2_emissions.at[year, 'CO2 emissions biomass'] = biomass_consumption * EMISSION_FACTOR_BIOMASS_QUEBEC
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CO2_emissions.at[year, 'CO2 emissions fueloil'] = fuel_oil_consumption * EMISSION_FACTOR_FUEL_OIL_QUEBEC
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CO2_emissions.at[year, 'CO2 emissions diesel'] = diesel_consumption * EMISSION_FACTOR_DIESEL_QUEBEC
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CO2_emissions_total = CO2_emissions.sum()
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