62 lines
3.2 KiB
Python
62 lines
3.2 KiB
Python
import pandas as pd
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from scripts.geojson_creator import process_geojson
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from pathlib import Path
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from hub.imports.geometry_factory import GeometryFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.results_factory import ResultFactory
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from scripts import random_assignation
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
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from scripts.costs.cost import Cost
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from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
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from hub.exports.exports_factory import ExportsFactory
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# Specify the GeoJSON file path
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location = [45.49034212153445, -73.61435648647083]
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geojson_file = process_geojson(x=location[1], y=location[0], diff=0.0001)
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file_path = (Path(__file__).parent / 'input_files' / 'processed_output -single_building.geojson')
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# Specify the output path for the PDF file
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output_path = (Path(__file__).parent / 'out_files').resolve()
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# Create city object from GeoJSON file
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city = GeometryFactory('geojson',
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path=file_path,
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height_field='height',
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year_of_construction_field='year_of_construction',
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function_field='function',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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# Enrich city data
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ConstructionFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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ResultFactory('energy_plus_multiple_buildings', city, output_path).enrich()
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ExportsFactory('sra', city, output_path).export()
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random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
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EnergySystemsFactory('montreal_future', city).enrich()
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for building in city.buildings:
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EnergySystemsSimulationFactory('archetype13', building=building, output_path=output_path).enrich()
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sum_floor_area = 0
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buildings_list = []
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for building in city.buildings:
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buildings_list.append(building.name)
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df = pd.DataFrame(columns=['building_name', 'total_floor_area', 'investment_cost', 'lc CAPEX'])
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df['building_name'] = buildings_list
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for building in city.buildings:
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for thermal_zone in building.thermal_zones_from_internal_zones:
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sum_floor_area += thermal_zone.total_floor_area
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costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
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costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
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output_path / f'{building.name}_cc.csv')
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investment_cost = costs.loc['global_capital_costs',
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f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
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lcc_capex = costs.loc['total_capital_costs_systems', f'Scenario {SYSTEM_RETROFIT_AND_PV}']
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df.loc[df['building_name'] == building.name, 'total_floor_area'] = (
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building.thermal_zones_from_internal_zones[0].total_floor_area)
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df.loc[df['building_name'] == building.name, 'investment_cost'] = investment_cost
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df.loc[df['building_name'] == building.name, 'lc CAPEX'] = lcc_capex
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df.to_csv(output_path / 'economic analysis.csv')
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