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a_gabald
summer_course_2024/processor.py

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import json
from pathlib import Path
import pandas as pd
import geopandas as gpd
#%%-----------------------------------------------
# """This code takes different results from energy plus with different building uses and obtains a unique geojson that combines all of them"""
#%% # -----------------------------------------------
#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=output_path / '1990_and_2019'/'building_type' / 'updated_buildings.geojson'
path2=output_path / '1990_and_2019'/ 'building_type_2' / 'updated_buildings.geojson'
path3=output_path / '1990_and_2019'/'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}")
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