summer_course_2024/processor.py

import json
from pathlib import Path
import pandas as pd
import geopandas as gpd


#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
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, "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, building_type_data, building_type_data2, building_type_data3):
    new_demands = {}

    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


# 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}")