346 lines
19 KiB
Python
346 lines
19 KiB
Python
import json
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from pathlib import Path
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import pandas as pd
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import geopandas as gpd
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#define output path
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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# Define output folders
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output_folders = ['building_type', 'building_type_2', 'building_type_3']
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#output paths containing energy+ results are read
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path1=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson'
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path2=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_2\updated_buildings.geojson'
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path3=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_3\updated_buildings.geojson'
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with open(path1, 'r') as f:
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building_type_data = json.load(f)
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with open(path2, 'r') as f:
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building_type_data2 = json.load(f)
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with open(path3, 'r') as f:
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building_type_data3 = 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 a function to calculate new demands based on percentages
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def calculate_demands(building_id, percentages, building_type_data, building_type_data2, building_type_data3):
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new_demands = {}
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for idx, feature in enumerate(building_type_data['features']):
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if feature['properties']['id'] == str(building_id):
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for demand_type in [
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'heating_demand_kWh', '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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demand_list_1 = building_type_data['features'][idx]['properties'].get(demand_type, [0])
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# Initialize demand lists for type 2 and type 3 with zeros
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demand_list_2 = [0] * len(demand_list_1)
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demand_list_3 = [0] * len(demand_list_1)
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# Update demand_list_2 if percentages['type2_%'] is not zero
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if percentages['type2_%'] != 0:
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for feature2 in building_type_data2['features']:
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if feature2['properties']['id'] == str(building_id):
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demand_list_2 = feature2['properties'].get(demand_type, [0])
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break
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# Update demand_list_3 if percentages['type3_%'] is not zero
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if percentages['type3_%'] != 0:
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for feature3 in building_type_data3['features']:
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if feature3['properties']['id'] == str(building_id):
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demand_list_3 = feature3['properties'].get(demand_type, [0])
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break
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# Ensure the demand lists are the same length by padding with zeros if necessary
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max_len = max(len(demand_list_1), len(demand_list_2), len(demand_list_3))
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demand_list_1 += [0] * (max_len - len(demand_list_1))
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demand_list_2 += [0] * (max_len - len(demand_list_2))
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demand_list_3 += [0] * (max_len - len(demand_list_3))
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new_demand_list = [
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demand_list_1[i] * percentages['type1_%'] +
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demand_list_2[i] * percentages['type2_%'] +
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demand_list_3[i] * percentages['type3_%']
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for i in range(max_len)
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]
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new_demands[demand_type] = new_demand_list
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return new_demands
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# Calculate demand per square meter for each building
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def calculate_demand_per_m2(building_type_data, building_type_data2, building_type_data3, percentage_data):
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results = {}
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for building_id, percentages in percentage_data.items():
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print(percentages ["total_floor_area"])
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new_demands = calculate_demands(building_id, percentages, building_type_data, building_type_data2,
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building_type_data3)
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for idx, feature in enumerate(building_type_data['features']):
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if feature['properties']['id'] == str(building_id):
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total_floor_area = percentages ["total_floor_area"]
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results[building_id] = {
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'roof_area':percentages["roof_area"],
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'total_floor_area': percentages["total_floor_area"],
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'heating_demand_kWh':new_demands.get('heating_demand_kWh', []),
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'cooling_demand_kWh':new_demands.get('cooling_demand_kWh', []),
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'domestic_hot_water_heat_demand_kWh': new_demands.get('domestic_hot_water_heat_demand_kWh', []),
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'appliances_electrical_demand_kWh': new_demands.get('appliances_electrical_demand_kWh', []),
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'lighting_electrical_demand_kWh': new_demands.get('lighting_electrical_demand_kWh', []),
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'heating_demand_kWh_per_m2': sum(new_demands.get('heating_demand_kWh', [])) / total_floor_area,
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'cooling_demand_kWh_per_m2': sum(new_demands.get('cooling_demand_kWh', [])) / total_floor_area,
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'domestic_hot_water_heat_demand_kWh_per_m2': sum(
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new_demands.get('domestic_hot_water_heat_demand_kWh', [])) / total_floor_area,
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'appliances_electrical_demand_kWh_per_m2': sum(
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new_demands.get('appliances_electrical_demand_kWh', [])) / total_floor_area,
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'lighting_electrical_demand_kWh_per_m2': sum(
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new_demands.get('lighting_electrical_demand_kWh', [])) / total_floor_area,
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'appliances_peak_load_kW': max(
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new_demands.get('appliances_electrical_demand_kWh', [])),
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'domestic_hot_water_peak_load_kW': max(
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new_demands.get('domestic_hot_water_heat_demand_kWh', [])) ,
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'heating_peak_load_kW': max(new_demands.get('heating_demand_kWh', [])),
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'cooling_peak_load_kW': max(new_demands.get('cooling_demand_kWh', [])),
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'lighting_peak_load_kW': max(
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new_demands.get('lighting_electrical_demand_kWh', [])),
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}
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return results
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# Example usage
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new_demands = calculate_demand_per_m2(building_type_data, building_type_data2, building_type_data3, percentage_data)
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# for building_id, demand_data in results.items():
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# print(f"Building ID: {building_id}")
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# for demand_type, value in demand_data.items():
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# print(f" {demand_type}: {value}")
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# new_demands = {}
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# for building_id, percentages in percentage_data.items():
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# print(building_id)
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# new_demands[building_id] = calculate_demands(building_id, percentages, building_type_data, building_type_data2, building_type_data3)
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# #
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import pandas as pd
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# Export the DataFrame to an Excel file
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output_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\new_demands.xlsx'
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# Create an Excel writer object
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with pd.ExcelWriter(output_path, engine='xlsxwriter') as writer:
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for building_id, demands in new_demands.items():
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# Convert demands to a DataFrame
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df_demands = pd.DataFrame(demands)
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# Convert building_id to string and check its length
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sheet_name = str(building_id)
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if len(sheet_name) > 31:
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sheet_name = sheet_name[:31] # Truncate to 31 characters if necessary
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# Write the DataFrame to a specific sheet named after the building_id
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df_demands.to_excel(writer, sheet_name=sheet_name, index=False)
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import matplotlib.pyplot as plt
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import numpy as np
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# Assume calculate_demand_per_m2 and results have been computed as before
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def extract_demands(data, building_id, demand_type):
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for feature in data['features']:
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if feature['properties']['id'] == str(building_id):
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return feature['properties'].get(demand_type, [0])
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return 0
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# Create a list of building IDs
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building_ids = list(percentage_data.keys())
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# Define the demand types to be plotted
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demand_types = [
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'heating_demand_kWh_per_m2',
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'cooling_demand_kWh_per_m2',
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'domestic_hot_water_heat_demand_kWh_per_m2',
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'appliances_electrical_demand_kWh_per_m2',
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'lighting_electrical_demand_kWh_per_m2'
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]
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# Initialize lists to store values for plotting
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new_demands_values = {demand_type: [] for demand_type in demand_types}
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building_type_data_values = {demand_type: [] for demand_type in demand_types}
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building_type_data2_values = {demand_type: [] for demand_type in demand_types}
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building_type_data3_values = {demand_type: [] for demand_type in demand_types}
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# Populate the lists with the corresponding values
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for building_id in building_ids:
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for demand_type in demand_types:
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new_demands_values[demand_type].append(new_demands[building_id][demand_type])
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building_type_data_values[demand_type].append(extract_demands(building_type_data, building_id, demand_type))
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building_type_data2_values[demand_type].append(extract_demands(building_type_data2, building_id, demand_type))
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building_type_data3_values[demand_type].append(extract_demands(building_type_data3, building_id, demand_type))
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# Plot the values for each demand type
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for demand_type in demand_types:
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x = np.arange(len(building_ids)) # the label locations
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width = 0.2 # the width of the bars
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fig, ax = plt.subplots(figsize=(15, 7))
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ax.bar(x - 1.5 * width, new_demands_values[demand_type], width, label='New Demands')
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ax.bar(x - 0.5 * width, building_type_data_values[demand_type], width, label='Old Demands - Type 1')
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ax.bar(x + 0.5 * width, building_type_data2_values[demand_type], width, label='Old Demands - Type 2')
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ax.bar(x + 1.5 * width, building_type_data3_values[demand_type], width, label='Old Demands - Type 3')
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# Add some text for labels, title, and custom x-axis tick labels, etc.
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ax.set_xlabel('Building ID')
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ax.set_ylabel(f'{demand_type} (kWh/m²)')
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ax.set_title(f'Comparison of {demand_type} by Building ID')
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ax.set_xticks(x)
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ax.set_xticklabels(building_ids, rotation=90)
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ax.legend()
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fig.tight_layout()
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plt.show()
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# Load the existing GeoJSON file
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geojson_file_path =r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson' # Replace with the actual path
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with open(geojson_file_path, 'r') as f:
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geojson_data = json.load(f)
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# Define output path
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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# Load additional data from the CSV files
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solar_radiation_tilted_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\solar_radiation_tilted_selected_buildings.csv'
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solar_radiation_horizontal_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\solar_radiation_horizontal_selected_buildings.csv'
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import csv
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def load_solar_data(file_path, key_name):
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solar_data = {}
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with open(file_path, 'r') as f:
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reader = csv.reader(f)
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headers = next(reader)[1:] # Skip the first header and get building IDs
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for row in reader:
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for building_id, solar_value in zip(headers, row[1:]): # Skip the first column in each row
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building_id = int(building_id)
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solar_value = float(solar_value)
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if building_id not in solar_data:
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solar_data[building_id] = {}
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if key_name not in solar_data[building_id]:
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solar_data[building_id][key_name] = []
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solar_data[building_id][key_name].append(solar_value)
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return solar_data
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solar_radiation_tilted = load_solar_data(solar_radiation_tilted_path, 'solar_radiation_tilted')
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solar_radiation_horizontal = load_solar_data(solar_radiation_horizontal_path, 'solar_radiation_horizontal')
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# Merge the solar data into a single dictionary
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solar_data = {}
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for building_id in set(solar_radiation_tilted.keys()).union(solar_radiation_horizontal.keys()):
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solar_data[building_id] = {}
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if building_id in solar_radiation_tilted:
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solar_data[building_id]['solar_radiation_tilted'] = solar_radiation_tilted[building_id][
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'solar_radiation_tilted']
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if building_id in solar_radiation_horizontal:
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solar_data[building_id]['solar_radiation_horizontal'] = solar_radiation_horizontal[building_id][
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'solar_radiation_horizontal']
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# Example data for results (use actual results data in practice)
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results = {
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1646: {
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"appliances_peak_load_kW": 269.020198862769,
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"domestic_hot_water_peak_load_kW": 1477.6420222203596,
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"heating_peak_load_kW": 1650.4097261870008,
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"cooling_peak_load_kW": 1441.1027982978985,
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"lighting_peak_load_kW": 295.92221874904584,
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"heating_demand_kWh_per_m2": 40.4922413088911,
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"cooling_demand_kWh_per_m2": 20.33948801154316,
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"domestic_hot_water_heat_demand_kWh_per_m2": 60.25215210051923,
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"appliances_electrical_demand_kWh_per_m2": 19.352038723954898,
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"lighting_electrical_demand_kWh_per_m2": 11.246090428260413,
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}
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# Add similar dictionaries for other building IDs
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}
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|
|
|
# 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}")
|
|
|
|
|