feature: add simultinity factor calculations

main.py

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+from scripts.geojson_creator import process_geojson
+from pathlib import Path
+import subprocess
+from 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
+from hub.imports.results_factory import ResultFactory
+from scripts.energy_system_analysis_report import EnergySystemAnalysisReport
+from scripts import random_assignation
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from scripts.energy_system_sizing import SystemSizing
+from scripts.energy_system_retrofit_results import system_results, new_system_results
+from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
+from scripts.costs.cost import Cost
+from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV
+import hub.helpers.constants as cte
+from hub.exports.exports_factory import ExportsFactory
+# Specify the GeoJSON file path
+# location = [45.49034212153445, -73.61435648647083]
+# geojson_file = process_geojson(x=location[1], y=location[0], diff=0.0005)
+file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
+# Specify the output path for the PDF file
+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)
+
+for building in city.buildings:
+    print(building.heating_demand[cte.MONTH])
+    print(building.cooling_demand[cte.MONTH])
+
+processor = DemandShiftProcessor(city)
+
+processor.process_demands()
+
+print(city.simultaneity_factor)
+for building in city.buildings:
+    print(building.heating_demand[cte.MONTH])
+    print(building.cooling_demand[cte.MONTH])
+

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 2024
+        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