cost performance improvements

costs/cost.py

@@ -5,6 +5,8 @@ Copyright © 2023 Project Coder Guille Gutierrez guillermo.gutierrezmorote@conco
 Code contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
 Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
 """
+import datetime
+
 import pandas as pd
 import numpy_financial as npf
 from hub.city_model_structure.building import Building
@@ -72,12 +74,22 @@ class Cost:
     :return: DataFrame
     """
     results = pd.DataFrame()
+    start = datetime.datetime.now()
     global_capital_costs, global_capital_incomes = CapitalCosts(self._building, self._configuration).calculate()
+    print(f'CapitalCosts: {datetime.datetime.now()-start}')
+    start = datetime.datetime.now()
     global_end_of_life_costs = EndOfLifeCosts(self._building, self._configuration).calculate()
+    print(f'EndOfLifeCosts: {datetime.datetime.now() - start}')
+    start = datetime.datetime.now()
     global_operational_costs = TotalOperationalCosts(self._building, self._configuration).calculate()
+    print(f'TotalOperationalCosts: {datetime.datetime.now() - start}')
+    start = datetime.datetime.now()
     global_maintenance_costs = TotalMaintenanceCosts(self._building, self._configuration).calculate()
+    print(f'TotalMaintenanceCosts: {datetime.datetime.now() - start}')
+    start = datetime.datetime.now()
     global_operational_incomes = TotalOperationalIncomes(self._building, self._configuration).calculate()
-
+    print(f'OperationalIncomes: {datetime.datetime.now() - start}')
+    start = datetime.datetime.now()
     df_capital_costs_skin = (
         global_capital_costs['B2010_opaque_walls'] +
         global_capital_costs['B2020_transparent'] +
@@ -151,5 +163,5 @@ class Cost:
       'global_maintenance_costs',
       'global_operational_incomes'
     ]
-
+    print(f'Dataframes: {datetime.datetime.now()-start}')
     return results

costs/peak_load.py

@@ -28,22 +28,25 @@ class PeakLoad:
     heating = 0
     cooling = 0
     for system in self._building.energy_systems:
-      for demand_type in system.demand_types:
+      if cte.HEATING in system.demand_types:
-        if demand_type == cte.HEATING:
+        heating = 1
-          heating = 1
+      if cte.COOLING in system.demand_types:
-        if demand_type == cte.COOLING:
+        cooling = 1
-          cooling = 1
     if cte.MONTH in self._building.heating_peak_load.keys() and cte.MONTH in self._building.cooling_peak_load.keys():
       peak_lighting = self._building.lighting_peak_load[cte.YEAR][0]
       peak_appliances = self._building.appliances_peak_load[cte.YEAR][0]
       monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
-      conditioning_peak = []
+      conditioning_peak = max(self._building.heating_peak_load[cte.MONTH], self._building.cooling_peak_load[cte.MONTH])
-      for i, value in enumerate(self._building.heating_peak_load[cte.MONTH]):
+      for i in range(len(conditioning_peak)):
-        if cooling * self._building.cooling_peak_load[cte.MONTH][i] > heating * value:
+        if cooling == 1 and heating == 1:
-          conditioning_peak.append(cooling * self._building.cooling_peak_load[cte.MONTH][i])
+          conditioning_peak[i] = conditioning_peak[i]
+          continue
+        elif cooling == 0:
+          conditioning_peak[i] = self._building.heating_peak_load[cte.MONTH][i] * heating
         else:
-          conditioning_peak.append(heating * value)
+          conditioning_peak[i] = self._building.cooling_peak_load[cte.MONTH][i] * cooling
         monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
+
       electricity_peak_load_results = pd.DataFrame(
         monthly_electricity_peak,
         columns=[f'electricity peak load W']

tests/unit_tests.py

@@ -42,14 +42,16 @@ class Initialize:
       sra_file = str((output_path / f'{city.name}_sra.xml').resolve())
       subprocess.run(['sra', sra_file], stdout=subprocess.DEVNULL)
       ResultFactory('sra', city, output_path).enrich()
-
+      print(f'sra completed {datetime.datetime.now() - start}')
       for building in city.buildings:
         building.energy_systems_archetype_name = 'system 1 gas pv'
       EnergySystemsFactory('montreal_custom', city).enrich()
+      print(f'energy systems completed {datetime.datetime.now() - start}')
       EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, output_path).export()
       _insel_files = glob.glob(f'{output_path}/*.insel')
       for insel_file in _insel_files:
         subprocess.run(['insel', str(insel_file)], stdout=subprocess.DEVNULL)
+      print(f'insel completed {datetime.datetime.now() - start}')
       ResultFactory('insel_monthly_energy_balance', city, output_path).enrich()
       self._city = city
       print(f'init completed {datetime.datetime.now() - start}')
@@ -61,7 +63,9 @@ class UnitTests(unittest.TestCase):
 
   def test_current_status(self):
     for building in self.init.city.buildings:
+      start = datetime.datetime.now()
       result = Cost(building).life_cycle
+      print(f'Cost calculation for building {building.name}: {datetime.datetime.now()-start}')
       self.assertIsNotNone(result)
       self.assertEqual(0, result.values[0])