import json from pathlib import Path import matplotlib.pyplot as plt # Specify the GeoJSON file path input_files_path = (Path(__file__).parent / 'input_files') # Load the updated GeoJSON file cotaining the results baseline_scenario_path =input_files_path / 'updated_buildings_with_all_data_baseline.geojson' energy_efficient_scenario_path = input_files_path / 'updated_buildings_with_all_data_2024.geojson' with open(baseline_scenario_path , 'r') as f: baseline_scenario = json.load(f) with open(energy_efficient_scenario_path, 'r') as f: energy_efficient_scenario=json.load(f) 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}, } # Define the demand types to be plotted demand_types = [ 'heating_demand_kWh', 'cooling_demand_kWh', 'domestic_hot_water_heat_demand_kWh', 'appliances_electrical_demand_kWh', 'lighting_electrical_demand_kWh' ] # # Function to extract demand data from the GeoJSON # def extract_demand_data(geojson_data, demand_type, period_start, period_end): # demand_data = {} # for feature in geojson_data['features']: # building_id = int(feature['properties']['id']) # if demand_type in feature['properties']: # demand_series = feature['properties'][demand_type][period_start:period_end] # demand_data[building_id] = demand_series # return demand_data # # # Define time periods # period_start_january = 0 # period_end_january = 168 # First week of January (0-168h) # period_start_summer = 3360 # period_end_summer = 3528 # One week in summer # # # Function to plot data for all buildings # def plot_all_buildings(data, period, title): # plt.figure(figsize=(15, 8)) # for building_id, values in data.items(): # plt.plot(range(len(values)), values, label=f'Building {building_id}') # plt.xlabel('Hour') # plt.ylabel('Energy Consumption (kWh)') # plt.title(title) # plt.legend(loc='upper right', bbox_to_anchor=(1.15, 1)) # plt.show() # # # Function to compare baseline and efficient scenarios for selected buildings # def plot_comparison(selected_buildings, baseline_data, efficient_data, period, title): # plt.figure(figsize=(15, 8)) # for building_id in selected_buildings: # baseline = baseline_data.get(building_id, []) # efficient = efficient_data.get(building_id, []) # plt.plot(range(len(baseline)), baseline, label=f'Baseline Building {building_id}') # plt.plot(range(len(efficient)), efficient, linestyle='--', label=f'Efficient Building {building_id}') # plt.xlabel('Hour') # plt.ylabel('Energy Consumption (kWh)') # plt.title(title) # plt.legend(loc='upper right', bbox_to_anchor=(1.15, 1)) # plt.show() # # # Loop through each demand type and extract data, then plot # for demand_type in demand_types: # # Extract demand data for the specified periods # baseline_january = extract_demand_data(baseline_scenario, demand_type, period_start_january, period_end_january) # baseline_summer = extract_demand_data(baseline_scenario, demand_type, period_start_summer, period_end_summer) # efficient_january = extract_demand_data(energy_efficient_scenario, demand_type, period_start_january, period_end_january) # efficient_summer = extract_demand_data(energy_efficient_scenario, demand_type, period_start_summer, period_end_summer) # # # Plot all buildings for the first week of January and one week in summer # plot_all_buildings(baseline_january, range(period_start_january, period_end_january), f'First Week of January - Baseline Scenario - {demand_type}') # plot_all_buildings(baseline_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Baseline Scenario - {demand_type}') # # # Selected buildings for comparison # selected_buildings = [1646, 1691, 1687] # # # Plot comparison for the first week of January and one week in summer # plot_comparison(selected_buildings, baseline_january, efficient_january, range(period_start_january, period_end_january), f'First Week of January - Comparison - {demand_type}') # plot_comparison(selected_buildings, baseline_summer, efficient_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Comparison - {demand_type}') # # Function to extract and sum demand data from the GeoJSON def extract_and_sum_demand_data(geojson_data, demand_type, period_start, period_end): district_demand = [0] * (period_end - period_start) for feature in geojson_data['features']: if demand_type in feature['properties']: demand_series = feature['properties'][demand_type][period_start:period_end] district_demand = [sum(x) for x in zip(district_demand, demand_series)] # Convert kWh to MWh district_demand_mwh = [value / 1000 for value in district_demand] return district_demand_mwh # Define time periods period_start_january = 0 period_end_january = 168 # First week of January (0-168h) period_start_summer = 3360 period_end_summer = 3528 # One week in summer # Function to plot district demand def plot_district_demand(district_demand, title): plt.figure(figsize=(15, 8)) plt.plot(range(len(district_demand)), district_demand, label='District Demand') plt.xlabel('Hour') plt.ylabel('Energy Consumption (MWh)') plt.title(title) plt.legend(loc='upper right') plt.show() # Function to compare district demands between baseline and efficient scenarios def plot_district_comparison(district_demand_baseline, district_demand_efficient, title): plt.figure(figsize=(15, 8)) plt.plot(range(len(district_demand_baseline)), district_demand_baseline, label='Baseline District Demand') plt.plot(range(len(district_demand_efficient)), district_demand_efficient, linestyle='--', label='Efficient District Demand') plt.xlabel('Hour') plt.ylabel('Energy Consumption (MWh)') plt.title(title) plt.legend(loc='upper right') plt.show() # # Loop through each demand type, extract and sum data, then plot # for demand_type in demand_types: # # Extract and sum demand data for the specified periods # district_demand_january_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start_january, period_end_january) # district_demand_summer_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start_summer, period_end_summer) # district_demand_january_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start_january, period_end_january) # district_demand_summer_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start_summer, period_end_summer) # # # Plot district demand for the first week of January and one week in summer for baseline scenario # plot_district_demand(district_demand_january_baseline, f'District Demand - First Week of January - Baseline Scenario - {demand_type}') # plot_district_demand(district_demand_summer_baseline, f'District Demand - One Week in Summer - Baseline Scenario - {demand_type}') # # # Plot district demand for the first week of January and one week in summer for energy-efficient scenario # plot_district_demand(district_demand_january_efficient, f'District Demand - First Week of January - Energy Efficient Scenario - {demand_type}') # plot_district_demand(district_demand_summer_efficient, f'District Demand - One Week in Summer - Energy Efficient Scenario - {demand_type}') # # # Plot comparison of district demand between baseline and energy-efficient scenarios # plot_district_comparison(district_demand_january_baseline, district_demand_january_efficient, f'District Demand Comparison - First Week of January - {demand_type}') # plot_district_comparison(district_demand_summer_baseline, district_demand_summer_efficient, f'District Demand Comparison - One Week in Summer - {demand_type}') # Function to extract and sum demand data from the GeoJSON # Define time period for a year period_start = 0 period_end = 8760 # Full year (0-8760h) # Dictionary to store the district demand data for export district_demand_data = {'hour': list(range(period_start, period_end))} import pandas as pd # Loop through each demand type, extract and sum data, then store in dictionary for demand_type in demand_types: # Extract and sum demand data for the full year district_demand_mwh_baseline = extract_and_sum_demand_data(baseline_scenario, demand_type, period_start, period_end) district_demand_mwh_efficient = extract_and_sum_demand_data(energy_efficient_scenario, demand_type, period_start, period_end) # Store data in the dictionary district_demand_data[f'{demand_type}_baseline'] = district_demand_mwh_baseline district_demand_data[f'{demand_type}_efficient'] = district_demand_mwh_efficient # Calculate total district demand values for the year total_district_baseline = sum(district_demand_mwh_baseline) total_district_efficient = sum(district_demand_mwh_efficient) # Print total district demand values print(f'Total District Demand for {demand_type} (Baseline): {total_district_baseline:.2f} MWh') print(f'Total District Demand for {demand_type} (Efficient): {total_district_efficient:.2f} MWh') # Convert dictionary to DataFrame and export to CSV district_demand_df = pd.DataFrame(district_demand_data) output_csv_path = input_files_path / 'district_demand.csv' district_demand_df.to_csv(output_csv_path, index=False) print(f'District demand data exported to {output_csv_path}')