cost completed

cerc_costs.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: cerc-costs
-Version: 0.1.0.0
+Version: 0.1.0.2
 Summary: CERC costs contains the basic cost calculation per CERC-Hub building
 Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
 Classifier: Programming Language :: Python

cerc_costs.egg-info/SOURCES.txt

@@ -1,3 +1,4 @@
+README.md
 pyproject.toml
 requirements.txt
 setup.py
@@ -14,6 +15,7 @@ costs/constants.py
 costs/cost.py
 costs/cost_base.py
 costs/end_of_life_costs.py
+costs/peak_load.py
 costs/total_maintenance_costs.py
 costs/total_operational_costs.py
 costs/total_operational_incomes.py

costs/capital_costs.py

@@ -81,16 +81,15 @@ class CapitalCosts(CostBase):
     capital_cost_other_hvac_ahu = 0
     capital_cost_lighting = 0
 
-    for internal_zone in self._building.internal_zones:
-      for thermal_zone in internal_zone.thermal_zones:
-        for thermal_boundary in thermal_zone.thermal_boundaries:
-          if thermal_boundary.type == 'Ground':
-            surface_ground += thermal_boundary.opaque_area
-          elif thermal_boundary.type == 'Roof':
-            surface_roof += thermal_boundary.opaque_area
-          elif thermal_boundary.type == 'Wall':
-            surface_opaque += thermal_boundary.opaque_area * (1 - thermal_boundary.window_ratio)
-            surface_transparent += thermal_boundary.opaque_area * thermal_boundary.window_ratio
+    for thermal_zone in self._building.thermal_zones:
+      for thermal_boundary in thermal_zone.thermal_boundaries:
+        if thermal_boundary.type == 'Ground':
+          surface_ground += thermal_boundary.opaque_area
+        elif thermal_boundary.type == 'Roof':
+          surface_roof += thermal_boundary.opaque_area
+        elif thermal_boundary.type == 'Wall':
+          surface_opaque += thermal_boundary.opaque_area * (1 - thermal_boundary.window_ratio)
+          surface_transparent += thermal_boundary.opaque_area * thermal_boundary.window_ratio
 
     peak_heating = self._building.heating_peak_load[cte.YEAR][0] / 1000
     peak_cooling = self._building.cooling_peak_load[cte.YEAR][0] / 1000

costs/cost.py

@@ -94,8 +94,6 @@ class Cost:
 
     df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
     df_operational_costs = (
-        global_operational_costs['Fixed_costs_electricity_peak'] +
-        global_operational_costs['Fixed_costs_electricity_monthly'] +
         global_operational_costs['Fixed_costs_electricity_peak'] +
         global_operational_costs['Fixed_costs_electricity_monthly'] +
         global_operational_costs['Variable_costs_electricity'] +

costs/cost_base.py

@@ -19,9 +19,8 @@ class CostBase:
     self._building = building
     self._configuration = configuration
     self._total_floor_area = 0
-    for internal_zone in building.internal_zones:
-      for thermal_zone in internal_zone.thermal_zones:
-        self._total_floor_area += thermal_zone.total_floor_area
+    for thermal_zone in building.thermal_zones:
+      self._total_floor_area += thermal_zone.total_floor_area
     self._archetype = None
     self._capital_costs_chapter = None
     for archetype in self._configuration.costs_catalog.entries().archetypes:

costs/peak_load.py

@@ -34,19 +34,8 @@ class PeakLoad:
         if demand_type == cte.COOLING:
           cooling = 1
     if cte.MONTH in self._building.heating_peak_load.keys() and cte.MONTH in self._building.cooling_peak_load.keys():
-      peak_lighting = 0
-      peak_appliances = 0
-      for thermal_zone in self._building.internal_zones[0].thermal_zones:
-        lighting = thermal_zone.lighting
-        for schedule in lighting.schedules:
-          peak = max(schedule.values) * lighting.density * thermal_zone.total_floor_area
-          if peak > peak_lighting:
-            peak_lighting = peak
-        appliances = thermal_zone.appliances
-        for schedule in appliances.schedules:
-          peak = max(schedule.values) * appliances.density * thermal_zone.total_floor_area
-          if peak > peak_appliances:
-            peak_appliances = peak
+      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 = []
       for i, value in enumerate(self._building.heating_peak_load[cte.MONTH]):
@@ -57,9 +46,8 @@ class PeakLoad:
         monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
       electricity_peak_load_results = pd.DataFrame(
         monthly_electricity_peak,
-        columns=[f'{self._building.name} electricity peak load W']
+        columns=[f'electricity peak load W']
       )
     else:
-      electricity_peak_load_results = pd.DataFrame(array, columns=[f'{self._building.name} electricity peak load W'])
-
+      electricity_peak_load_results = pd.DataFrame(array, columns=[f'electricity peak load W'])
     return electricity_peak_load_results

costs/total_operational_costs.py

@@ -85,8 +85,12 @@ class TotalOperationalCosts(CostBase):
       self._yearly_operational_costs.at[year, 'Fixed_costs_electricity_monthly'] = (
           monthly_electricity_cost_year_0 * price_increase_peak_electricity
       )
+      if not isinstance(variable_electricity_cost_year_0, pd.DataFrame):
+        variable_costs_electricity = variable_electricity_cost_year_0 * price_increase_electricity
+      else:
+        variable_costs_electricity = float(variable_electricity_cost_year_0.iloc[0] * price_increase_electricity)
       self._yearly_operational_costs.at[year, 'Variable_costs_electricity'] = (
-        float(variable_electricity_cost_year_0.iloc[0] * price_increase_electricity)
+        variable_costs_electricity
       )
       self._yearly_operational_costs.at[year, 'Fixed_costs_gas'] = fixed_gas_cost_year_0 * price_increase_gas
       self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (