fix: DHW system optimization tested and confirmed

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm.py

@@ -38,7 +38,7 @@ class MultiObjectiveGeneticAlgorithm:
       operators such as crossover and mutation rates.
   """
 
-  def __init__(self, population_size=50, generations=50, crossover_rate=0.9, mutation_rate=0.33,
+  def __init__(self, population_size=20, generations=20, crossover_rate=0.9, mutation_rate=0.33,
                number_of_selected_solutions=None, optimization_scenario=None):
     self.population_size = population_size
     self.population = []
@@ -804,9 +804,11 @@ class MultiObjectiveGeneticAlgorithm:
         storage_type = storage_component['type']
         capacity = storage_component['capacity']
         volume = storage_component['volume']
+        heating_coil = storage_component['heating_coil_capacity']
         selected_solutions[solution_type]['Storage Components'].append({'storage type': storage_type,
                                                                         'capacity (W)': capacity,
-                                                                        'volume (m3)': volume})
+                                                                        'volume (m3)': volume,
+                                                                        'heating coil capacity (W)': heating_coil})
       if 'energy-consumption' in self.optimization_scenario:
         selected_solutions[solution_type]['total energy consumption kWh'] = individual['total_energy_consumption']
       if 'cost' in self.optimization_scenario:

simulation_test.py

@@ -0,0 +1,58 @@
+from pathlib import Path
+import subprocess
+from building_modelling.ep_run_enrich import energy_plus_workflow
+from energy_system_modelling_package.energy_system_modelling_factories.montreal_energy_system_archetype_modelling_factory import \
+  MontrealEnergySystemArchetypesSimulationFactory
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.multi_objective_genetic_algorithm import MultiObjectiveGeneticAlgorithm
+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
+import hub.helpers.constants as cte
+from building_modelling.geojson_creator import process_geojson
+from energy_system_modelling_package import random_assignation
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from energy_system_modelling_package.energy_system_modelling_factories.energy_system_sizing_factory import \
+  EnergySystemsSizingFactory
+from energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_results import consumption_data, \
+  cost_data
+from costing_package.cost import Cost
+from costing_package.constants import *
+from hub.exports.exports_factory import ExportsFactory
+
+# Specify the GeoJSON file path
+input_files_path = (Path(__file__).parent / 'input_files')
+input_files_path.mkdir(parents=True, exist_ok=True)
+geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.00006)
+geojson_file_path = input_files_path / 'output_buildings.geojson'
+output_path = (Path(__file__).parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+energy_plus_output_path = output_path / 'energy_plus_outputs'
+energy_plus_output_path.mkdir(parents=True, exist_ok=True)
+simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
+simulation_results_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+cost_analysis_output_path = output_path / 'cost_analysis'
+cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
+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()
+ExportsFactory('sra', city, sra_output_path).export()
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+ResultFactory('sra', city, sra_output_path).enrich()
+energy_plus_workflow(city, energy_plus_output_path)
+random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
+EnergySystemsFactory('montreal_future', city).enrich()
+for building in city.buildings:
+  design_period_demands = MultiObjectiveGeneticAlgorithm(optimization_scenario='energy-consumption_cost').design_period_identification(city.buildings[0])
+  heating_demand = design_period_demands[cte.HEATING]['demands']

test.py

@@ -54,5 +54,5 @@ energy_plus_workflow(city, energy_plus_output_path)
 random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
 EnergySystemsFactory('montreal_future', city).enrich()
 for building in city.buildings:
-  energy_system = building.energy_systems[1]
+  energy_system = building.energy_systems[-1]
   MultiObjectiveGeneticAlgorithm(optimization_scenario='energy-consumption_cost').solve_ga(building, energy_system)