20240722 changes
This commit is contained in:
Andrea Gabaldon Moreno
parent
76a6f5f930
commit
0ce76bef13
16
.gitignore
vendored
16
.gitignore
vendored
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@ -11,3 +11,19 @@
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**/.idea/
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cerc_hub.egg-info
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/out_files
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hub/data/construction/nrcan_constructions.json
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hub/helpers/data/hub_function_to_nrcan_construction_function.py
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hub/imports/construction/nrcan_physics_parameters.py
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input_files/Lachine_moved_2019.geojson
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input_files/Lachine_moved_2024.geojson
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input_files/Lachine_moved_existing_year.geojson
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input_files/Lachine_moved_existing_year_type_2.geojson
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input_files/Lachine_moved_existing_year_type_3.geojson
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input_files/district_demand.csv
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input_files/Lachine_moved_2024_type.geojson
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input_files/Lachine_moved_2024_type_2.geojson
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input_files/Lachine_moved_2024_type_3.geojson
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input_files/new_demands_existing_year.xlsx
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input_files/updated_buildings_with_all_data_2024.geojson
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input_files/updated_buildings_with_all_data_baseline.geojson
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.spyproject
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215
comparing.py
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comparing.py
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@ -0,0 +1,215 @@
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import json
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from pathlib import Path
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import matplotlib.pyplot as plt
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# Specify the GeoJSON file path
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input_files_path = (Path(__file__).parent / 'input_files')
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# Load the updated GeoJSON file cotaining the results
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baseline_scenario_path =input_files_path / 'updated_buildings_with_all_data_baseline.geojson'
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energy_efficient_scenario_path = input_files_path / 'updated_buildings_with_all_data_2024.geojson'
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with open(baseline_scenario_path , 'r') as f:
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baseline_scenario = json.load(f)
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with open(energy_efficient_scenario_path, 'r') as f:
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energy_efficient_scenario=json.load(f)
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percentage_data = {
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1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
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1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
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1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
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1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
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1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
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1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
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1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
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1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
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1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
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1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
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1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
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1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
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1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
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1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
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1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
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1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
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1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
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1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
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1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
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1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
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1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
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1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
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1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
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1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
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1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
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1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
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1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
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1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
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1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
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1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
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1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
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1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
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1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
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1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
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1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
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1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
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1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
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1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
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}
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# Define the demand types to be plotted
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demand_types = [
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'heating_demand_kWh',
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'cooling_demand_kWh',
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'domestic_hot_water_heat_demand_kWh',
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'appliances_electrical_demand_kWh',
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'lighting_electrical_demand_kWh'
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]
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# # Function to extract demand data from the GeoJSON
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# def extract_demand_data(geojson_data, demand_type, period_start, period_end):
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# demand_data = {}
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# for feature in geojson_data['features']:
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# building_id = int(feature['properties']['id'])
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# if demand_type in feature['properties']:
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# demand_series = feature['properties'][demand_type][period_start:period_end]
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# demand_data[building_id] = demand_series
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# return demand_data
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#
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# # Define time periods
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# period_start_january = 0
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# period_end_january = 168 # First week of January (0-168h)
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# period_start_summer = 3360
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# period_end_summer = 3528 # One week in summer
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#
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# # Function to plot data for all buildings
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# def plot_all_buildings(data, period, title):
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# plt.figure(figsize=(15, 8))
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# for building_id, values in data.items():
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# plt.plot(range(len(values)), values, label=f'Building {building_id}')
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# plt.xlabel('Hour')
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# plt.ylabel('Energy Consumption (kWh)')
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# plt.title(title)
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# plt.legend(loc='upper right', bbox_to_anchor=(1.15, 1))
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# plt.show()
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#
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# # Function to compare baseline and efficient scenarios for selected buildings
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# def plot_comparison(selected_buildings, baseline_data, efficient_data, period, title):
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# plt.figure(figsize=(15, 8))
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# for building_id in selected_buildings:
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# baseline = baseline_data.get(building_id, [])
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# efficient = efficient_data.get(building_id, [])
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# plt.plot(range(len(baseline)), baseline, label=f'Baseline Building {building_id}')
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# plt.plot(range(len(efficient)), efficient, linestyle='--', label=f'Efficient Building {building_id}')
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# plt.xlabel('Hour')
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# plt.ylabel('Energy Consumption (kWh)')
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# plt.title(title)
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# plt.legend(loc='upper right', bbox_to_anchor=(1.15, 1))
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# plt.show()
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#
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# # Loop through each demand type and extract data, then plot
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# for demand_type in demand_types:
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# # Extract demand data for the specified periods
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# baseline_january = extract_demand_data(baseline_scenario, demand_type, period_start_january, period_end_january)
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# baseline_summer = extract_demand_data(baseline_scenario, demand_type, period_start_summer, period_end_summer)
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# efficient_january = extract_demand_data(energy_efficient_scenario, demand_type, period_start_january, period_end_january)
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# efficient_summer = extract_demand_data(energy_efficient_scenario, demand_type, period_start_summer, period_end_summer)
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#
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# # Plot all buildings for the first week of January and one week in summer
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# plot_all_buildings(baseline_january, range(period_start_january, period_end_january), f'First Week of January - Baseline Scenario - {demand_type}')
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# plot_all_buildings(baseline_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Baseline Scenario - {demand_type}')
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#
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# # Selected buildings for comparison
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# selected_buildings = [1646, 1691, 1687]
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#
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# # Plot comparison for the first week of January and one week in summer
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# plot_comparison(selected_buildings, baseline_january, efficient_january, range(period_start_january, period_end_january), f'First Week of January - Comparison - {demand_type}')
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# plot_comparison(selected_buildings, baseline_summer, efficient_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Comparison - {demand_type}')
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#
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# Function to extract and sum demand data from the GeoJSON
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def extract_and_sum_demand_data(geojson_data, demand_type, period_start, period_end):
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district_demand = [0] * (period_end - period_start)
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for feature in geojson_data['features']:
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if demand_type in feature['properties']:
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demand_series = feature['properties'][demand_type][period_start:period_end]
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district_demand = [sum(x) for x in zip(district_demand, demand_series)]
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# Convert kWh to MWh
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district_demand_mwh = [value / 1000 for value in district_demand]
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return district_demand_mwh
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# Define time periods
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period_start_january = 0
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period_end_january = 168 # First week of January (0-168h)
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period_start_summer = 3360
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period_end_summer = 3528 # One week in summer
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# Function to plot district demand
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def plot_district_demand(district_demand, title):
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plt.figure(figsize=(15, 8))
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plt.plot(range(len(district_demand)), district_demand, label='District Demand')
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plt.xlabel('Hour')
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plt.ylabel('Energy Consumption (MWh)')
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plt.title(title)
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plt.legend(loc='upper right')
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plt.show()
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# Function to compare district demands between baseline and efficient scenarios
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def plot_district_comparison(district_demand_baseline, district_demand_efficient, title):
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plt.figure(figsize=(15, 8))
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plt.plot(range(len(district_demand_baseline)), district_demand_baseline, label='Baseline District Demand')
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plt.plot(range(len(district_demand_efficient)), district_demand_efficient, linestyle='--', label='Efficient District Demand')
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plt.xlabel('Hour')
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plt.ylabel('Energy Consumption (MWh)')
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plt.title(title)
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plt.legend(loc='upper right')
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plt.show()
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# # Loop through each demand type, extract and sum data, then plot
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# for demand_type in demand_types:
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# # Extract and sum demand data for the specified periods
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# district_demand_january_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start_january, period_end_january)
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# district_demand_summer_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start_summer, period_end_summer)
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# district_demand_january_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start_january, period_end_january)
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# district_demand_summer_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start_summer, period_end_summer)
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#
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# # Plot district demand for the first week of January and one week in summer for baseline scenario
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# plot_district_demand(district_demand_january_baseline, f'District Demand - First Week of January - Baseline Scenario - {demand_type}')
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# plot_district_demand(district_demand_summer_baseline, f'District Demand - One Week in Summer - Baseline Scenario - {demand_type}')
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#
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# # Plot district demand for the first week of January and one week in summer for energy-efficient scenario
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# plot_district_demand(district_demand_january_efficient, f'District Demand - First Week of January - Energy Efficient Scenario - {demand_type}')
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# plot_district_demand(district_demand_summer_efficient, f'District Demand - One Week in Summer - Energy Efficient Scenario - {demand_type}')
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#
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# # Plot comparison of district demand between baseline and energy-efficient scenarios
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# plot_district_comparison(district_demand_january_baseline, district_demand_january_efficient, f'District Demand Comparison - First Week of January - {demand_type}')
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# plot_district_comparison(district_demand_summer_baseline, district_demand_summer_efficient, f'District Demand Comparison - One Week in Summer - {demand_type}')
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# Function to extract and sum demand data from the GeoJSON
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# Define time period for a year
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period_start = 0
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period_end = 8760 # Full year (0-8760h)
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# Dictionary to store the district demand data for export
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district_demand_data = {'hour': list(range(period_start, period_end))}
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import pandas as pd
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# Loop through each demand type, extract and sum data, then store in dictionary
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for demand_type in demand_types:
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# Extract and sum demand data for the full year
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district_demand_mwh_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start, period_end)
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district_demand_mwh_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start,
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period_end)
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# Store data in the dictionary
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district_demand_data[f'{demand_type}_baseline'] = district_demand_mwh_baseline
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district_demand_data[f'{demand_type}_efficient'] = district_demand_mwh_efficient
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# Calculate total district demand values for the year
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total_district_baseline = sum(district_demand_mwh_baseline)
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total_district_efficient = sum(district_demand_mwh_efficient)
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# Print total district demand values
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print(f'Total District Demand for {demand_type} (Baseline): {total_district_baseline:.2f} MWh')
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print(f'Total District Demand for {demand_type} (Efficient): {total_district_efficient:.2f} MWh')
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# Convert dictionary to DataFrame and export to CSV
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district_demand_df = pd.DataFrame(district_demand_data)
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output_csv_path = input_files_path / 'district_demand.csv'
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district_demand_df.to_csv(output_csv_path, index=False)
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print(f'District demand data exported to {output_csv_path}')
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@ -415,8 +415,8 @@ class Building(CityObject):
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peak = max(schedule.values) * lighting.density * thermal_zone.total_floor_area
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if peak > peak_lighting:
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peak_lighting = peak
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results[cte.MONTH] = [peak for _ in range(0, 12)]
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results[cte.YEAR] = [peak]
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results[cte.MONTH] = [float(peak) for _ in range(0, 12)]
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results[cte.YEAR] = [float(peak)]
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return results
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@property
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@ -434,8 +434,8 @@ class Building(CityObject):
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peak = max(schedule.values) * appliances.density * thermal_zone.total_floor_area
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if peak > peak_appliances:
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peak_appliances = peak
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results[cte.MONTH] = [peak for _ in range(0, 12)]
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results[cte.YEAR] = [peak]
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results[cte.MONTH] = [float(peak) for _ in range(0, 12)]
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results[cte.YEAR] = [float(peak)]
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return results
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@property
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69
main.py
69
main.py
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@ -13,12 +13,12 @@ input_files_path = (Path(__file__).parent / 'input_files')
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building_type_2_modelling=2
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#'Lachine_New_Developments.geojson'
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geojson_file_path = input_files_path / 'Lachine_moved_existing_year_type.geojson'
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geojson_file_path = input_files_path / 'Lachine_moved_2024_type.geojson'
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if building_type_2_modelling==1:
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gdf = gpd.read_file(geojson_file_path)
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# Filter gdf when 'building_type_2' is not null
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filtered_gdf = gdf[gdf['building_type_2'].notnull()]
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output_geojson =input_files_path /'Lachine_moved_existing_year_type_2.geojson'
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output_geojson =input_files_path /'Lachine_moved_2024_type_2.geojson'
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geojson_file_path =output_geojson
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filtered_gdf.to_file(output_geojson, driver='GeoJSON')
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print(f"New GeoJSON saved in: {output_geojson}")
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gdf = gpd.read_file(geojson_file_path)
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# Filter gdf when 'building_type_3' is not null
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filtered_gdf = gdf[gdf['building_type_3'].notnull()]
|
||||
output_geojson =input_files_path /'Lachine_moved_existing_year_type_3.geojson'
|
||||
output_geojson =input_files_path /'Lachine_moved_2024_type_3.geojson'
|
||||
geojson_file_path = output_geojson
|
||||
filtered_gdf.to_file(output_geojson, driver='GeoJSON')
|
||||
print(f"New GeoJSON saved in: {output_geojson}")
|
||||
|
|
@ -48,6 +48,8 @@ WeatherFactory('epw', city).enrich()
|
|||
energy_plus_workflow(city)
|
||||
def to_dict(building, total_floor_area):
|
||||
return {
|
||||
'roof_area': building.floor_area,
|
||||
'total_floor_area': total_floor_area,
|
||||
'year_of_construction' : building.year_of_construction,
|
||||
'type_function':building.function,
|
||||
'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
|
||||
|
|
@ -61,11 +63,11 @@ def to_dict(building, total_floor_area):
|
|||
'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
|
||||
'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
|
||||
'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
|
||||
'heating_demand_kWh_per_m2' : building.heating_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||
'cooling_demand_kWh_per_m2' : building.cooling_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||
'domestic_hot_water_heat_demand_kWh_per_m2':building.domestic_hot_water_heat_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||
'appliances_electrical_demand_kWh_per_m2':building.appliances_electrical_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||
'lighting_electrical_demand_kWh_per_m2': building.lighting_electrical_demand[cte.YEAR][0]/ (3.6e6 * total_floor_area),
|
||||
'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
|
||||
'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
|
||||
'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
|
||||
|
|
@ -74,6 +76,7 @@ def to_dict(building, total_floor_area):
|
|||
}
|
||||
buildings_dic={}
|
||||
|
||||
|
||||
for building in city.buildings:
|
||||
|
||||
total_floor_area = 0
|
||||
|
|
@ -126,31 +129,31 @@ with open(updated_geojson_file_path, 'w') as f:
|
|||
json.dump(geojson_data, f, indent=4)
|
||||
|
||||
|
||||
EnergySystemsFactory('montreal_custom', city).enrich()
|
||||
print('test')
|
||||
for building in city.buildings:
|
||||
energy_systems = building.energy_systems
|
||||
for energy_system in energy_systems:
|
||||
generation_units = energy_system.generation_systems
|
||||
if cte.HEATING in energy_system.demand_types:
|
||||
for generation_unit in generation_units:
|
||||
generation_unit.heat_efficiency = 0.96
|
||||
# EnergySystemsFactory('montreal_custom', city).enrich()
|
||||
# print('test')
|
||||
# for building in city.buildings:
|
||||
# building.function = cte.COMMERCIAL
|
||||
#
|
||||
# ConstructionFactory('nrcan', city).enrich()
|
||||
# UsageFactory('nrcan', city).enrich()
|
||||
# energy_plus_workflow(city)
|
||||
# for building in city.buildings:
|
||||
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
||||
# print(building.name)
|
||||
# total_floor_area = 0
|
||||
# for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||
# total_floor_area += thermal_zone.total_floor_area
|
||||
# print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
||||
# energy_systems = building.energy_systems
|
||||
# for energy_system in energy_systems:
|
||||
# generation_units = energy_system.generation_systems
|
||||
# if cte.HEATING in energy_system.demand_types:
|
||||
# for generation_unit in generation_units:
|
||||
# generation_unit.heat_efficiency = 0.96
|
||||
# # for building in city.buildings:
|
||||
# # building.function = cte.COMMERCIAL
|
||||
# #
|
||||
# # ConstructionFactory('nrcan', city).enrich()
|
||||
# # UsageFactory('nrcan', city).enrich()
|
||||
# # energy_plus_workflow(city)
|
||||
# # for building in city.buildings:
|
||||
# # print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
||||
# # print(building.name)
|
||||
# # total_floor_area = 0
|
||||
# # for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||
# # total_floor_area += thermal_zone.total_floor_area
|
||||
# # print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
||||
|
||||
|
||||
for building in city.buildings:
|
||||
print(building.name)
|
||||
print(building.year_of_construction)
|
||||
print(building.usages_percentage)
|
||||
# for building in city.buildings:
|
||||
# print(building.name)
|
||||
# print(building.year_of_construction)
|
||||
# print(building.usages_percentage)
|
||||
176
main_results_in_geojson.py
Normal file
176
main_results_in_geojson.py
Normal file
|
|
@ -0,0 +1,176 @@
|
|||
from pathlib import Path
|
||||
from scripts.ep_workflow import energy_plus_workflow
|
||||
from hub.helpers.monthly_values import MonthlyValues
|
||||
from hub.imports.geometry_factory import GeometryFactory
|
||||
from hub.helpers.dictionaries import Dictionaries
|
||||
from hub.imports.construction_factory import ConstructionFactory
|
||||
from hub.imports.usage_factory import UsageFactory
|
||||
from hub.imports.weather_factory import WeatherFactory
|
||||
import hub.helpers.constants as cte
|
||||
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
||||
from hub.helpers.peak_loads import PeakLoads
|
||||
|
||||
import geopandas as gpd
|
||||
import json
|
||||
# Specify the GeoJSON file path
|
||||
input_files_path = (Path(__file__).parent / 'input_files')
|
||||
output_path = (Path(__file__).parent / 'out_files').resolve()
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
geojson_file_path = output_path / 'updated_buildings_with_all_data.geojson'
|
||||
with open(geojson_file_path , 'r') as f:
|
||||
building_type_data = json.load(f)
|
||||
|
||||
|
||||
|
||||
# Create city object from GeoJSON file
|
||||
city = GeometryFactory('geojson',
|
||||
path=geojson_file_path,
|
||||
height_field='maximum_roof_height',
|
||||
year_of_construction_field='year_built',
|
||||
function_field='building_type',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
# Enrich city data
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
|
||||
# #energy plus is not going to be processed here, as demand has been obtained before
|
||||
# energy_plus_workflow(city)
|
||||
percentage_data = {
|
||||
1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
|
||||
1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
|
||||
1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
|
||||
1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
|
||||
1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
|
||||
1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
|
||||
1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
|
||||
1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
|
||||
1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
|
||||
1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
|
||||
1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
|
||||
1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
|
||||
1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
|
||||
1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
|
||||
1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
|
||||
1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
|
||||
1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
|
||||
1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
|
||||
1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
|
||||
1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
|
||||
1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
|
||||
1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
|
||||
1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
|
||||
1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
|
||||
1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
|
||||
1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
|
||||
1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
|
||||
1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
|
||||
1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
|
||||
1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
|
||||
1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
|
||||
1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
|
||||
1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
|
||||
1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
|
||||
1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
|
||||
1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
|
||||
1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
|
||||
1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
|
||||
}
|
||||
|
||||
def enrich_buildings_with_geojson_data (building_type_data, city):
|
||||
for building in city.buildings:
|
||||
for idx, feature in enumerate(building_type_data['features']):
|
||||
if feature['properties']['id'] == str(building.name):
|
||||
building.heating_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('heating_demand_kWh', [0])]
|
||||
building.cooling_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('cooling_demand_kWh', [0])]
|
||||
building.domestic_hot_water_heat_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('domestic_hot_water_heat_demand_kWh', [0])]
|
||||
building.appliances_electrical_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('appliances_electrical_demand_kWh', [0])]
|
||||
building.lighting_electrical_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('lighting_electrical_demand_kWh', [0])]
|
||||
building.heating_demand[cte.MONTH] = MonthlyValues.get_total_month(building.heating_demand[cte.HOUR])
|
||||
building.cooling_demand[cte.MONTH] = MonthlyValues.get_total_month(building.cooling_demand[cte.HOUR])
|
||||
building.domestic_hot_water_heat_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.domestic_hot_water_heat_demand[cte.HOUR]))
|
||||
building.appliances_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.appliances_electrical_demand[cte.HOUR]))
|
||||
building.lighting_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.lighting_electrical_demand[cte.HOUR]))
|
||||
building.heating_demand[cte.YEAR] = [sum(building.heating_demand[cte.MONTH])]
|
||||
building.cooling_demand[cte.YEAR] = [sum(building.cooling_demand[cte.MONTH])]
|
||||
building.domestic_hot_water_heat_demand[cte.YEAR] = [sum(building.domestic_hot_water_heat_demand[cte.MONTH])]
|
||||
building.appliances_electrical_demand[cte.YEAR] = [sum(building.appliances_electrical_demand[cte.MONTH])]
|
||||
building.lighting_electrical_demand[cte.YEAR] = [sum(building.lighting_electrical_demand[cte.MONTH])]
|
||||
|
||||
|
||||
|
||||
enrich_buildings_with_geojson_data (building_type_data, city)
|
||||
# for building in city.buildings:
|
||||
# monthly_values = PeakLoads().peak_loads_from_hourly(building.lighting_electrical_demand[cte.HOUR])
|
||||
# building.lighting_peak_load[cte.MONTH]=[x / cte.WATTS_HOUR_TO_JULES for x in monthly_values]
|
||||
# building.lighting_peak_load[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
|
||||
# monthly_values = PeakLoads().peak_loads_from_hourly(building.appliances_electrical_demand[cte.HOUR])
|
||||
# building.appliances_electrical_demand[cte.MONTH]=[x / cte.WATTS_HOUR_TO_JULES for x in monthly_values]
|
||||
# building.appliances_electrical_demand[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
|
||||
|
||||
|
||||
|
||||
|
||||
# def to_dict(building, total_floor_area):
|
||||
# return {
|
||||
# 'roof_area': building.floor_area,
|
||||
# 'total_floor_area': total_floor_area,
|
||||
# 'year_of_construction' : building.year_of_construction,
|
||||
# 'type_function':building.function,
|
||||
# 'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
|
||||
# 'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6),
|
||||
# 'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6),
|
||||
# 'average_storey_height_meters': building.average_storey_height,
|
||||
# 'max_height_meters_meters': building.max_height,
|
||||
# 'global_horizontal_kWh_per_m2': sum(building.global_horizontal[cte.HOUR])/ (3.6e6),
|
||||
# 'appliances_peak_load_kW':building.appliances_peak_load[cte.YEAR][0]/ (1e3),
|
||||
# 'domestic_hot_water_peak_load_kW': building.domestic_hot_water_peak_load[cte.YEAR][0]/ (1e3),
|
||||
# 'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
|
||||
# 'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
|
||||
# 'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
|
||||
# 'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
# 'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
# 'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
# 'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
# 'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
|
||||
# 'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
|
||||
# 'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
|
||||
# 'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
|
||||
# 'appliances_electrical_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
|
||||
# 'lighting_electrical_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
|
||||
# }
|
||||
# buildings_dic={}
|
||||
|
||||
|
||||
for building in city.buildings:
|
||||
|
||||
total_floor_area = 0
|
||||
for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||
total_floor_area += thermal_zone.total_floor_area
|
||||
print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
||||
building.energy_systems_archetype_name = 'system 1 gas'
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
EnergySystemsFactory('montreal_custom', city).enrich()
|
||||
print('test')
|
||||
for building in city.buildings:
|
||||
energy_systems = building.energy_systems
|
||||
for energy_system in energy_systems:
|
||||
generation_units = energy_system.generation_systems
|
||||
if cte.HEATING in energy_system.demand_types:
|
||||
for generation_unit in generation_units:
|
||||
generation_unit.heat_efficiency = 0.96
|
||||
|
||||
|
||||
# for building in city.buildings:
|
||||
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
||||
# print(building.name)
|
||||
# total_floor_area = 0
|
||||
# for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||
# total_floor_area += thermal_zone.total_floor_area
|
||||
# print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
||||
|
||||
446
processor.py
446
processor.py
|
|
@ -2,144 +2,344 @@ import json
|
|||
from pathlib import Path
|
||||
import pandas as pd
|
||||
import geopandas as gpd
|
||||
# Specify the GeoJSON file path
|
||||
input_files_path = (Path(__file__).parent / 'input_files')
|
||||
geojson_file_path = input_files_path / 'Lachine_moved_existing_year_type.geojson'
|
||||
#read it to use later
|
||||
gdf = gpd.read_file(geojson_file_path)
|
||||
|
||||
|
||||
#define output path
|
||||
output_path = (Path(__file__).parent / 'out_files').resolve()
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
# Define output folders
|
||||
output_folders = ['building_type', 'building_type_2', 'building_type_3']
|
||||
#output paths containing energy+ results are read
|
||||
|
||||
gdf_building_outputs={}
|
||||
|
||||
output_paths = {}
|
||||
# Create directories for the output folders
|
||||
for folder in output_folders:
|
||||
path = output_path / folder
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
output_paths[folder] = path
|
||||
# Read the resulting GeoDataFrames saved in each folder
|
||||
gdf_building_outputs = {}
|
||||
for folder in output_folders:
|
||||
path = output_paths[folder] / 'updated_buildings.geojson'
|
||||
if path.exists():
|
||||
gdf_building_outputs[folder] = gpd.read_file(path)
|
||||
else:
|
||||
gdf_building_outputs[folder] = gpd.GeoDataFrame()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
path1=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson'
|
||||
path2=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_2\updated_buildings.geojson'
|
||||
path3=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_3\updated_buildings.geojson'
|
||||
with open(path1, 'r') as f:
|
||||
building_type_data = json.load(f)
|
||||
with open(path2, 'r') as f:
|
||||
building_type_data2 = json.load(f)
|
||||
with open(path3, 'r') as f:
|
||||
building_type_data3 = json.load(f)
|
||||
percentage_data = {
|
||||
1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0},
|
||||
1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0},
|
||||
1668: {"type1_%": 0.666666667, "type2_%": 0.333333333, "type3_%": 0},
|
||||
1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1673: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1674: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1675: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0},
|
||||
1678: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1679: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1680: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1681: {"type1_%": 0.666666667, "type2_%": 0.3, "type3_%": 0},
|
||||
1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475},
|
||||
1688: {"type1_%": 0.92, "type2_%": 0.1, "type3_%": 0},
|
||||
1689: {"type1_%": 0.96, "type2_%": 0, "type3_%": 0},
|
||||
1690: {"type1_%": 0.94, "type2_%": 0.1, "type3_%": 0},
|
||||
1691: {"type1_%": 0.75, "type2_%": 0.3, "type3_%": 0},
|
||||
1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
|
||||
1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
|
||||
1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
|
||||
1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
|
||||
1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
|
||||
1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
|
||||
1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
|
||||
1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
|
||||
1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
|
||||
1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
|
||||
1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
|
||||
1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
|
||||
1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
|
||||
1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
|
||||
1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
|
||||
1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
|
||||
1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
|
||||
1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
|
||||
1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
|
||||
1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
|
||||
1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
|
||||
1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
|
||||
1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
|
||||
1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
|
||||
1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
|
||||
1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
|
||||
1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
|
||||
1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
|
||||
1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
|
||||
1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
|
||||
1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
|
||||
1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
|
||||
1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
|
||||
1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
|
||||
1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
|
||||
1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
|
||||
1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
|
||||
1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
|
||||
}
|
||||
|
||||
|
||||
# Define a function to calculate new demands based on percentages
|
||||
def calculate_demands(building_id, percentages, gdf_building_type, gdf_building_type_2, gdf_building_type_3):
|
||||
def calculate_demands(building_id, percentages, building_type_data, building_type_data2, building_type_data3):
|
||||
new_demands = {}
|
||||
for demand_type in [
|
||||
'heating_demand_kWh', 'cooling_demand_kWh',
|
||||
'domestic_hot_water_heat_demand_kWh',
|
||||
'appliances_electrical_demand_kWh',
|
||||
'lighting_electrical_demand_kWh']:
|
||||
demand_type_1 = gdf_building_type.loc[gdf_building_type['id'] == building_id, demand_type].values[
|
||||
0] if not gdf_building_type.empty else 0
|
||||
demand_type_2 = gdf_building_type_2.loc[gdf_building_type_2['id'] == building_id, demand_type].values[
|
||||
0] if not gdf_building_type_2.empty else 0
|
||||
demand_type_3 = gdf_building_type_3.loc[gdf_building_type_3['id'] == building_id, demand_type].values[
|
||||
0] if not gdf_building_type_3.empty else 0
|
||||
|
||||
new_demand = (
|
||||
demand_type_1 * percentages['type1_%'] +
|
||||
demand_type_2 * percentages['type2_%'] +
|
||||
demand_type_3 * percentages['type3_%']
|
||||
)
|
||||
new_demands[demand_type] = new_demand
|
||||
for idx, feature in enumerate(building_type_data['features']):
|
||||
if feature['properties']['id'] == str(building_id):
|
||||
for demand_type in [
|
||||
'heating_demand_kWh', 'cooling_demand_kWh',
|
||||
'domestic_hot_water_heat_demand_kWh',
|
||||
'appliances_electrical_demand_kWh',
|
||||
'lighting_electrical_demand_kWh']:
|
||||
|
||||
demand_list_1 = building_type_data['features'][idx]['properties'].get(demand_type, [0])
|
||||
|
||||
# Initialize demand lists for type 2 and type 3 with zeros
|
||||
demand_list_2 = [0] * len(demand_list_1)
|
||||
demand_list_3 = [0] * len(demand_list_1)
|
||||
|
||||
# Update demand_list_2 if percentages['type2_%'] is not zero
|
||||
if percentages['type2_%'] != 0:
|
||||
for feature2 in building_type_data2['features']:
|
||||
if feature2['properties']['id'] == str(building_id):
|
||||
demand_list_2 = feature2['properties'].get(demand_type, [0])
|
||||
break
|
||||
|
||||
# Update demand_list_3 if percentages['type3_%'] is not zero
|
||||
if percentages['type3_%'] != 0:
|
||||
for feature3 in building_type_data3['features']:
|
||||
if feature3['properties']['id'] == str(building_id):
|
||||
demand_list_3 = feature3['properties'].get(demand_type, [0])
|
||||
break
|
||||
|
||||
# Ensure the demand lists are the same length by padding with zeros if necessary
|
||||
max_len = max(len(demand_list_1), len(demand_list_2), len(demand_list_3))
|
||||
demand_list_1 += [0] * (max_len - len(demand_list_1))
|
||||
demand_list_2 += [0] * (max_len - len(demand_list_2))
|
||||
demand_list_3 += [0] * (max_len - len(demand_list_3))
|
||||
|
||||
new_demand_list = [
|
||||
demand_list_1[i] * percentages['type1_%'] +
|
||||
demand_list_2[i] * percentages['type2_%'] +
|
||||
demand_list_3[i] * percentages['type3_%']
|
||||
for i in range(max_len)
|
||||
]
|
||||
new_demands[demand_type] = new_demand_list
|
||||
|
||||
return new_demands
|
||||
|
||||
|
||||
# Process each building in the GeoJSON
|
||||
for idx, feature in gdf.iterrows():
|
||||
building_id = feature['id'] # Adjust this based on your GeoJSON structure
|
||||
if building_id == 1673:
|
||||
percentages = percentage_data[building_id]
|
||||
print(percentages)
|
||||
new_demands = calculate_demands(building_id, percentages,
|
||||
gdf_building_outputs.get('building_type', gpd.GeoDataFrame()),
|
||||
gdf_building_outputs.get('building_type_2', gpd.GeoDataFrame()),
|
||||
gdf_building_outputs.get('building_type_3', gpd.GeoDataFrame()))
|
||||
print(new_demands)
|
||||
# Update the properties of the feature
|
||||
gdf.at[idx, 'heating_demand_kWh'] = new_demands['heating_demand_kWh']
|
||||
gdf.at[idx, 'cooling_demand_kWh'] = new_demands['cooling_demand_kWh']
|
||||
gdf.at[idx, 'domestic_hot_water_heat_demand_kWh'] = new_demands['domestic_hot_water_heat_demand_kWh']
|
||||
gdf.at[idx, 'appliances_electrical_demand_kWh'] = new_demands['appliances_electrical_demand_kWh']
|
||||
gdf.at[idx, 'lighting_electrical_demand_kWh'] = new_demands['lighting_electrical_demand_kWh']
|
||||
# Calculate demand per square meter for each building
|
||||
def calculate_demand_per_m2(building_type_data, building_type_data2, building_type_data3, percentage_data):
|
||||
results = {}
|
||||
|
||||
for building_id, percentages in percentage_data.items():
|
||||
print(percentages ["total_floor_area"])
|
||||
new_demands = calculate_demands(building_id, percentages, building_type_data, building_type_data2,
|
||||
building_type_data3)
|
||||
|
||||
for idx, feature in enumerate(building_type_data['features']):
|
||||
if feature['properties']['id'] == str(building_id):
|
||||
total_floor_area = percentages ["total_floor_area"]
|
||||
|
||||
results[building_id] = {
|
||||
'roof_area':percentages["roof_area"],
|
||||
'total_floor_area': percentages["total_floor_area"],
|
||||
'heating_demand_kWh':new_demands.get('heating_demand_kWh', []),
|
||||
'cooling_demand_kWh':new_demands.get('cooling_demand_kWh', []),
|
||||
'domestic_hot_water_heat_demand_kWh': new_demands.get('domestic_hot_water_heat_demand_kWh', []),
|
||||
'appliances_electrical_demand_kWh': new_demands.get('appliances_electrical_demand_kWh', []),
|
||||
'lighting_electrical_demand_kWh': new_demands.get('lighting_electrical_demand_kWh', []),
|
||||
'heating_demand_kWh_per_m2': sum(new_demands.get('heating_demand_kWh', [])) / total_floor_area,
|
||||
'cooling_demand_kWh_per_m2': sum(new_demands.get('cooling_demand_kWh', [])) / total_floor_area,
|
||||
'domestic_hot_water_heat_demand_kWh_per_m2': sum(
|
||||
new_demands.get('domestic_hot_water_heat_demand_kWh', [])) / total_floor_area,
|
||||
'appliances_electrical_demand_kWh_per_m2': sum(
|
||||
new_demands.get('appliances_electrical_demand_kWh', [])) / total_floor_area,
|
||||
'lighting_electrical_demand_kWh_per_m2': sum(
|
||||
new_demands.get('lighting_electrical_demand_kWh', [])) / total_floor_area,
|
||||
'appliances_peak_load_kW': max(
|
||||
new_demands.get('appliances_electrical_demand_kWh', [])),
|
||||
'domestic_hot_water_peak_load_kW': max(
|
||||
new_demands.get('domestic_hot_water_heat_demand_kWh', [])) ,
|
||||
'heating_peak_load_kW': max(new_demands.get('heating_demand_kWh', [])),
|
||||
'cooling_peak_load_kW': max(new_demands.get('cooling_demand_kWh', [])),
|
||||
'lighting_peak_load_kW': max(
|
||||
new_demands.get('lighting_electrical_demand_kWh', [])),
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
# Example usage
|
||||
new_demands = calculate_demand_per_m2(building_type_data, building_type_data2, building_type_data3, percentage_data)
|
||||
# for building_id, demand_data in results.items():
|
||||
# print(f"Building ID: {building_id}")
|
||||
# for demand_type, value in demand_data.items():
|
||||
# print(f" {demand_type}: {value}")
|
||||
# new_demands = {}
|
||||
# for building_id, percentages in percentage_data.items():
|
||||
# print(building_id)
|
||||
# new_demands[building_id] = calculate_demands(building_id, percentages, building_type_data, building_type_data2, building_type_data3)
|
||||
|
||||
# #
|
||||
import pandas as pd
|
||||
|
||||
# Export the DataFrame to an Excel file
|
||||
output_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\new_demands.xlsx'
|
||||
|
||||
# Create an Excel writer object
|
||||
with pd.ExcelWriter(output_path, engine='xlsxwriter') as writer:
|
||||
for building_id, demands in new_demands.items():
|
||||
# Convert demands to a DataFrame
|
||||
df_demands = pd.DataFrame(demands)
|
||||
# Convert building_id to string and check its length
|
||||
sheet_name = str(building_id)
|
||||
if len(sheet_name) > 31:
|
||||
sheet_name = sheet_name[:31] # Truncate to 31 characters if necessary
|
||||
# Write the DataFrame to a specific sheet named after the building_id
|
||||
df_demands.to_excel(writer, sheet_name=sheet_name, index=False)
|
||||
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# Assume calculate_demand_per_m2 and results have been computed as before
|
||||
|
||||
def extract_demands(data, building_id, demand_type):
|
||||
for feature in data['features']:
|
||||
if feature['properties']['id'] == str(building_id):
|
||||
return feature['properties'].get(demand_type, [0])
|
||||
return 0
|
||||
|
||||
# Create a list of building IDs
|
||||
building_ids = list(percentage_data.keys())
|
||||
|
||||
# Define the demand types to be plotted
|
||||
demand_types = [
|
||||
'heating_demand_kWh_per_m2',
|
||||
'cooling_demand_kWh_per_m2',
|
||||
'domestic_hot_water_heat_demand_kWh_per_m2',
|
||||
'appliances_electrical_demand_kWh_per_m2',
|
||||
'lighting_electrical_demand_kWh_per_m2'
|
||||
]
|
||||
|
||||
# Initialize lists to store values for plotting
|
||||
new_demands_values = {demand_type: [] for demand_type in demand_types}
|
||||
building_type_data_values = {demand_type: [] for demand_type in demand_types}
|
||||
building_type_data2_values = {demand_type: [] for demand_type in demand_types}
|
||||
building_type_data3_values = {demand_type: [] for demand_type in demand_types}
|
||||
|
||||
# Populate the lists with the corresponding values
|
||||
for building_id in building_ids:
|
||||
for demand_type in demand_types:
|
||||
new_demands_values[demand_type].append(new_demands[building_id][demand_type])
|
||||
building_type_data_values[demand_type].append(extract_demands(building_type_data, building_id, demand_type))
|
||||
building_type_data2_values[demand_type].append(extract_demands(building_type_data2, building_id, demand_type))
|
||||
building_type_data3_values[demand_type].append(extract_demands(building_type_data3, building_id, demand_type))
|
||||
|
||||
# Plot the values for each demand type
|
||||
for demand_type in demand_types:
|
||||
x = np.arange(len(building_ids)) # the label locations
|
||||
width = 0.2 # the width of the bars
|
||||
|
||||
fig, ax = plt.subplots(figsize=(15, 7))
|
||||
|
||||
ax.bar(x - 1.5 * width, new_demands_values[demand_type], width, label='New Demands')
|
||||
ax.bar(x - 0.5 * width, building_type_data_values[demand_type], width, label='Old Demands - Type 1')
|
||||
ax.bar(x + 0.5 * width, building_type_data2_values[demand_type], width, label='Old Demands - Type 2')
|
||||
ax.bar(x + 1.5 * width, building_type_data3_values[demand_type], width, label='Old Demands - Type 3')
|
||||
|
||||
# Add some text for labels, title, and custom x-axis tick labels, etc.
|
||||
ax.set_xlabel('Building ID')
|
||||
ax.set_ylabel(f'{demand_type} (kWh/m²)')
|
||||
ax.set_title(f'Comparison of {demand_type} by Building ID')
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(building_ids, rotation=90)
|
||||
ax.legend()
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
# Load the existing GeoJSON file
|
||||
geojson_file_path =r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson' # Replace with the actual path
|
||||
with open(geojson_file_path, 'r') as f:
|
||||
geojson_data = json.load(f)
|
||||
|
||||
# Define output path
|
||||
output_path = (Path(__file__).parent / 'out_files').resolve()
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Load additional data from the CSV files
|
||||
solar_radiation_tilted_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\solar_radiation_tilted_selected_buildings.csv'
|
||||
solar_radiation_horizontal_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\solar_radiation_horizontal_selected_buildings.csv'
|
||||
|
||||
|
||||
import csv
|
||||
|
||||
def load_solar_data(file_path, key_name):
|
||||
solar_data = {}
|
||||
with open(file_path, 'r') as f:
|
||||
reader = csv.reader(f)
|
||||
headers = next(reader)[1:] # Skip the first header and get building IDs
|
||||
|
||||
for row in reader:
|
||||
for building_id, solar_value in zip(headers, row[1:]): # Skip the first column in each row
|
||||
building_id = int(building_id)
|
||||
solar_value = float(solar_value)
|
||||
if building_id not in solar_data:
|
||||
solar_data[building_id] = {}
|
||||
if key_name not in solar_data[building_id]:
|
||||
solar_data[building_id][key_name] = []
|
||||
solar_data[building_id][key_name].append(solar_value)
|
||||
return solar_data
|
||||
|
||||
|
||||
solar_radiation_tilted = load_solar_data(solar_radiation_tilted_path, 'solar_radiation_tilted')
|
||||
solar_radiation_horizontal = load_solar_data(solar_radiation_horizontal_path, 'solar_radiation_horizontal')
|
||||
|
||||
# Merge the solar data into a single dictionary
|
||||
solar_data = {}
|
||||
for building_id in set(solar_radiation_tilted.keys()).union(solar_radiation_horizontal.keys()):
|
||||
solar_data[building_id] = {}
|
||||
if building_id in solar_radiation_tilted:
|
||||
solar_data[building_id]['solar_radiation_tilted'] = solar_radiation_tilted[building_id][
|
||||
'solar_radiation_tilted']
|
||||
if building_id in solar_radiation_horizontal:
|
||||
solar_data[building_id]['solar_radiation_horizontal'] = solar_radiation_horizontal[building_id][
|
||||
'solar_radiation_horizontal']
|
||||
|
||||
# Example data for results (use actual results data in practice)
|
||||
results = {
|
||||
1646: {
|
||||
"appliances_peak_load_kW": 269.020198862769,
|
||||
"domestic_hot_water_peak_load_kW": 1477.6420222203596,
|
||||
"heating_peak_load_kW": 1650.4097261870008,
|
||||
"cooling_peak_load_kW": 1441.1027982978985,
|
||||
"lighting_peak_load_kW": 295.92221874904584,
|
||||
"heating_demand_kWh_per_m2": 40.4922413088911,
|
||||
"cooling_demand_kWh_per_m2": 20.33948801154316,
|
||||
"domestic_hot_water_heat_demand_kWh_per_m2": 60.25215210051923,
|
||||
"appliances_electrical_demand_kWh_per_m2": 19.352038723954898,
|
||||
"lighting_electrical_demand_kWh_per_m2": 11.246090428260413,
|
||||
}
|
||||
# Add similar dictionaries for other building IDs
|
||||
}
|
||||
|
||||
# Update GeoJSON with percentage data, results, and solar data
|
||||
for feature in geojson_data['features']:
|
||||
building_id = int(feature['properties']['id'])
|
||||
|
||||
# Update with percentage data
|
||||
if building_id in percentage_data:
|
||||
for key, value in percentage_data[building_id].items():
|
||||
feature['properties'][key] = value
|
||||
|
||||
# Update with results data
|
||||
if building_id in new_demands:
|
||||
for key, value in new_demands[building_id].items():
|
||||
feature['properties'][key] = value
|
||||
|
||||
# Update with solar data
|
||||
if building_id in solar_data:
|
||||
for key, value in solar_data[building_id].items():
|
||||
feature['properties'][key] = value
|
||||
# Calculate and add the sums
|
||||
feature['properties']['sum_solar_radiation_tilted_kWh_per_m2'] = sum(solar_data[building_id]['solar_radiation_tilted']) / 3.6e6
|
||||
feature['properties']['sum_solar_radiation_horizontal_kWh_per_m2'] = sum(solar_data[building_id]['solar_radiation_horizontal']) / 3.6e6
|
||||
# Add 8760 arrays divided by 3.6e6
|
||||
feature['properties']['solar_radiation_tilted_kWh_per_m2'] = [value / 3.6e6 for value in solar_data[building_id]['solar_radiation_tilted']]
|
||||
feature['properties']['solar_radiation_horizontal_kWh_per_m2'] = [value / 3.6e6 for value in solar_data[building_id]['solar_radiation_horizontal']]
|
||||
|
||||
# Save the updated GeoJSON data to a new file
|
||||
updated_geojson_file_path = output_path / 'updated_buildings_with_all_data.geojson'
|
||||
with open(updated_geojson_file_path, 'w') as f:
|
||||
json.dump(geojson_data, f)
|
||||
|
||||
print(f"Updated GeoJSON data saved to {updated_geojson_file_path}")
|
||||
|
||||
print('test')
|
||||
|
||||
#
|
||||
# if building_id in percentage_data:
|
||||
# percentages = percentage_data[building_id]
|
||||
# new_demands = calculate_demands(building_id, percentages,
|
||||
# gdf_building_outputs.get('building_type', gpd.GeoDataFrame()),
|
||||
# gdf_building_outputs.get('building_type_2', gpd.GeoDataFrame()),
|
||||
# gdf_building_outputs.get('building_type_3', gpd.GeoDataFrame()))
|
||||
#
|
||||
# # Update the properties of each feature
|
||||
# gdf.at[idx, 'heating_demand_kWh'] = new_demands['heating_demand_kWh']
|
||||
# gdf.at[idx, 'cooling_demand_kWh'] = new_demands['cooling_demand_kWh']
|
||||
# gdf.at[idx, 'domestic_hot_water_heat_demand_kWh'] = new_demands['domestic_hot_water_heat_demand_kWh']
|
||||
# gdf.at[idx, 'appliances_electrical_demand_kWh'] = new_demands['appliances_electrical_demand_kWh']
|
||||
# gdf.at[idx, 'lighting_electrical_demand_kWh'] = new_demands['lighting_electrical_demand_kWh']
|
||||
#
|
||||
# # Save the updated GeoDataFrame to the appropriate folder
|
||||
# for folder, key in zip(output_folders, ['type1_%', 'type2_%', 'type3_%']):
|
||||
# if percentages[key] > 0:
|
||||
# output_gdf = gdf_building_outputs[folder]
|
||||
# output_gdf = output_gdf.append(gdf.iloc[[idx]], ignore_index=True)
|
||||
# output_gdf.to_file(output_paths[folder] / 'buildings.geojson', driver='GeoJSON')
|
||||
#
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user