feature: add simultinity factor calculations

main.py

@@ -0,0 +1,83 @@
+from pathlib import Path
+from scripts.district_heating_network.directory_manager import DirectoryManager
+import subprocess
+from scripts.ep_run_enrich import energy_plus_workflow
+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.exports.exports_factory import ExportsFactory
+from scripts.pv_feasibility import pv_feasibility
+import matplotlib.pyplot as plt
+from scripts.district_heating_network.district_heating_network_creator import DistrictHeatingNetworkCreator
+from scripts.district_heating_network.road_processor import road_processor
+from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory
+
+base_path = Path(__file__).parent
+dir_manager = DirectoryManager(base_path)
+
+# Input files directory
+input_files_path = dir_manager.create_directory('input_files')
+geojson_file_path = input_files_path / 'output_buildings.geojson'
+
+# Output files directory
+output_path = dir_manager.create_directory('out_files')
+
+# Subdirectories for output files
+energy_plus_output_path = dir_manager.create_directory('out_files/energy_plus_outputs')
+simulation_results_path = dir_manager.create_directory('out_files/simulation_results')
+sra_output_path = dir_manager.create_directory('out_files/sra_outputs')
+cost_analysis_output_path = dir_manager.create_directory('out_files/cost_analysis')
+
+# Select city area
+location = [45.53067276979674, -73.70234652694087]
+process_geojson(x=location[1], y=location[0], diff=0.001)
+
+# Create city object
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
+                       height_field='height',
+                       year_of_construction_field='year_of_construction',
+                       function_field='function',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+# WeatherFactory('epw', city).enrich()
+# energy_plus_workflow(city, energy_plus_output_path)
+# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
+# city.buildings[0].function = cte.COMMERCIAL
+# ConstructionFactory('nrcan', city).enrich()
+# UsageFactory('nrcan', city).enrich()
+# energy_plus_workflow(city, energy_plus_output_path)
+# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
+# city.buildings[0].function = cte.MEDIUM_OFFICE
+# ConstructionFactory('nrcan', city).enrich()
+# UsageFactory('nrcan', city).enrich()
+# energy_plus_workflow(city, energy_plus_output_path)
+# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
+# categories = list(data.keys())
+# values = list(data.values())
+# # Plotting
+# fig, ax = plt.subplots(figsize=(10, 6), dpi=96)
+# fig.suptitle('Impact of different usages on yearly heating demand', fontsize=16, weight='bold', alpha=.8)
+# ax.bar(categories, values, color=['#2196f3', '#ff5a5f', '#4caf50'], width=0.6, zorder=2)
+# ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
+# ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
+# ax.set_xlabel('Building Type', fontsize=12, labelpad=10)
+# ax.set_ylabel('Energy Consumption (MWh)', fontsize=14, labelpad=10)
+# ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
+# ax.set_xticks(np.arange(len(categories)))
+# ax.set_xticklabels(categories, rotation=45, ha='right')
+# ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=0)
+# ax.spines[['top', 'left', 'bottom']].set_visible(False)
+# ax.spines['right'].set_linewidth(1.1)
+# # Set a white background
+# fig.patch.set_facecolor('white')
+# # Adjust the margins around the plot area
+# plt.subplots_adjust(left=0.1, right=0.9, top=0.85, bottom=0.25)
+# # Save the plot
+# plt.savefig('plot_nrcan.png', bbox_inches='tight')
+# plt.close()
+print('test')

scripts/district_heating_network/simultinity_factor.py

@@ -0,0 +1,64 @@
+import pandas as pd
+import numpy as np
+
+
+class DemandShiftProcessor:
+  def __init__(self, city):
+    self.city = city
+
+  def random_shift(self, series):
+    shift_amount = np.random.randint(0, 2)
+    return series.shift(shift_amount).fillna(series.shift(shift_amount - len(series)))
+
+  def process_demands(self):
+    building_dfs = []
+
+    for building in self.city.buildings:
+      df = self.convert_building_to_dataframe(building)
+      df.set_index('Date/Time', inplace=True)
+      shifted_demands = df.apply(self.random_shift, axis=0)
+      self.update_building_demands(building, shifted_demands)
+      building_dfs.append(shifted_demands)
+
+    combined_df = pd.concat(building_dfs, axis=1)
+    self.calculate_and_set_simultaneity_factor(combined_df)
+
+  def convert_building_to_dataframe(self, building):
+    data = {
+      "Date/Time": self.generate_date_time_index(),
+      "Heating_Demand": building.heating_demand["hour"],
+      "Cooling_Demand": building.cooling_demand["hour"]
+    }
+    return pd.DataFrame(data)
+
+  def generate_date_time_index(self):
+    # Generate hourly date time index for a full year in 2013
+    date_range = pd.date_range(start="2013-01-01 00:00:00", end="2013-12-31 23:00:00", freq='H')
+    return date_range.strftime('%m/%d %H:%M:%S').tolist()
+
+  def update_building_demands(self, building, shifted_demands):
+    heating_shifted = shifted_demands["Heating_Demand"]
+    cooling_shifted = shifted_demands["Cooling_Demand"]
+
+    building.heating_demand = self.calculate_new_demands(heating_shifted)
+    building.cooling_demand = self.calculate_new_demands(cooling_shifted)
+
+  def calculate_new_demands(self, shifted_series):
+    new_demand = {
+      "hour": shifted_series.tolist(),
+      "month": self.calculate_monthly_demand(shifted_series),
+      "year": [shifted_series.sum()]
+    }
+    return new_demand
+
+  def calculate_monthly_demand(self, series):
+    series.index = pd.to_datetime(series.index, format='%m/%d %H:%M:%S')
+    monthly_demand = series.resample('M').sum()
+    return monthly_demand.tolist()
+
+  def calculate_and_set_simultaneity_factor(self, combined_df):
+    total_demand_original = combined_df.sum(axis=1)
+    peak_total_demand_original = total_demand_original.max()
+    individual_peak_demands = combined_df.max(axis=0)
+    sum_individual_peak_demands = individual_peak_demands.sum()
+    self.city.simultaneity_factor = peak_total_demand_original / sum_individual_peak_demands