feature: add simultinity factor calculations

main.py

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+from pathlib import Path
+from scripts.district_heating_network.simultinity_factor import DemandShiftProcessor
+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
+
+file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
+
+output_path = (Path(__file__).parent / 'out_files').resolve()
+
+# Create city object from GeoJSON file
+city = GeometryFactory('geojson',
+                       path=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
+
+# Enrich city data
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+WeatherFactory('epw', city).enrich()
+
+energy_plus_workflow(city)
+
+processor = DemandShiftProcessor(city)
+processor.process_demands()

scripts/district_heating_network/simultinity_factor.py

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+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