Fix plot errors with embodied emissions

architecture-building-systems · Sep 9, 2023 · 2d582d6 · 2d582d6
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Showing 7 changed files with 17 additions and 17 deletions.
diff --git a/cea/analysis/lca/embodied.py b/cea/analysis/lca/embodied.py
@@ -36,7 +36,7 @@ def lca_embodied(year_to_calculate, locator):
     The results are provided in total as well as per square meter:
 
     - embodied non-renewable primary energy: E_nre_pen_GJ and E_nre_pen_MJm2
-    - embodied greenhouse gas emissions: GHG_sys_embodied_tonCO2 and GHG_sys_embodied_kgCO2m2
+    - embodied greenhouse gas emissions: GHG_sys_embodied_tonCO2yr and GHG_sys_embodied_kgCO2m2yr
 
     As part of the algorithm, the following files are read from InputLocator:
 

diff --git a/cea/plots/comparisons/Annual_emissions.py b/cea/plots/comparisons/Annual_emissions.py
@@ -25,7 +25,7 @@ def __init__(self, project, parameters, cache):
         super(ComparisonsAnnualEmissionsPlot, self).__init__(project, parameters, cache)
         self.analysis_fields = ["GHG_sys_district_scale_tonCO2",
                                 "GHG_sys_building_scale_tonCO2",
-                                "GHG_sys_embodied_tonCO2",
+                                "GHG_sys_embodied_tonCO2yr",
                                 ]
         self.normalization = self.parameters['normalization']
         self.input_files = [(x[4].get_optimization_slave_total_performance, [x[3], x[2]]) if x[2] != "today" else

diff --git a/cea/plots/comparisons/__init__.py b/cea/plots/comparisons/__init__.py
@@ -90,7 +90,7 @@ def preprocessing_annual_emissions_scenarios(self):
 
             if generation == "today":
                 data_building_emissions = pd.read_csv(locator_scenario.get_lca_operation()).sum(axis=0)
-                data_building_emissions['GHG_sys_embodied_tonCO2'] = pd.read_csv(self.locator.get_lca_embodied())['GHG_sys_embodied_tonCO2'].sum()
+                data_building_emissions['GHG_sys_embodied_tonCO2yr'] = pd.read_csv(self.locator.get_lca_embodied())['GHG_sys_embodied_tonCO2yr'].sum()
                 data_raw_df = pd.DataFrame(data_building_emissions).T
                 data_raw_df = self.normalize_data_emissions(data_raw_df, self.normalization, self.analysis_fields)
             else:
@@ -132,29 +132,29 @@ def normalize_data_emissions(self, data_processed, normalization, analysis_field
                 lambda x: x / normalization_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         elif normalization == "net floor area":
             data = pd.read_csv(self.locator.get_total_demand())
             normalization_factor = sum(data['Aocc_m2'])
             data_processed = data_processed.apply(
                 lambda x: x / normalization_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         elif normalization == "air conditioned floor area":
             data = pd.read_csv(self.locator.get_total_demand())
             normalization_factor = sum(data['Af_m2'])
             data_processed = data_processed.apply(
                 lambda x: x / normalization_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         elif normalization == "building occupancy":
             data = pd.read_csv(self.locator.get_total_demand())
             normalization_factor = sum(data['people0'])
             data_processed = data_processed.apply(
                 lambda x: x / normalization_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         return data_processed
diff --git a/cea/plots/naming.csv b/cea/plots/naming.csv
@@ -439,9 +439,9 @@ Emissions and Primary Energy,GHG_sys_district_scale_tonCO2,ton CO2-eq/yr,Annual
 Emissions and Primary Energy,PEN_sys_building_scale_MJoil,MJ oil-eq/yr,Annual primary energy of building-scale systems,red_light
 Emissions and Primary Energy,PEN_sys_district_scale_MJoil,MJ oil-eq/yr,Annual primary energy of district-scale system,red
 
-Emissions and Primary Energy,GHG_sys_embodied_tonCO2,ton CO2-eq/yr,Annual building Embodied Emissions,grey_light
+Emissions and Primary Energy,GHG_sys_embodied_tonCO2yr,ton CO2-eq/yr,Annual building Embodied Emissions,grey_light
 Emissions and Primary Energy,O_ghg_ton,ton CO2-eq/yr,Energy system operation,grey
-Emissions and Primary Energy,GHG_sys_embodied_kgCO2m2,kg CO2-eq/m2.yr,Annual building Embodied Emissions,black
+Emissions and Primary Energy,GHG_sys_embodied_kgCO2m2yr,kg CO2-eq/m2.yr,Annual building Embodied Emissions,black
 Emissions and Primary Energy,GHG_sys_kgCO2m2,kg CO2-eq/m2.yr,Energy system operation,grey
 
 Thermal Network,P_loss_%,-,Pumping energy,grey

diff --git a/cea/plots/optimization/__init__.py b/cea/plots/optimization/__init__.py
@@ -70,7 +70,7 @@ def process_generation_total_performance_pareto_with_multi(self):
     @cea.plots.cache.cached
     def process_generation_total_performance_pareto(self):
         data_processed = pd.read_csv(self.locator.get_optimization_generation_total_performance_pareto(self.generation))
-        data_processed['GHG_sys_embodied_tonCO2'] = pd.read_csv(self.locator.get_lca_embodied())['GHG_sys_embodied_tonCO2'].sum()
+        data_processed['GHG_sys_embodied_tonCO2yr'] = pd.read_csv(self.locator.get_lca_embodied())['GHG_sys_embodied_tonCO2yr'].sum()
         return data_processed
 
     def normalize_data(self, data_processed, normalization, analysis_fields):
@@ -81,31 +81,31 @@ def normalize_data(self, data_processed, normalization, analysis_fields):
                 lambda x: x / normalizatioon_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         elif normalization == "net floor area":
             data = pd.read_csv(self.locator.get_total_demand())
             normalizatioon_factor = sum(data['Aocc_m2'])
             data_processed = data_processed.apply(
                 lambda x: x / normalizatioon_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         elif normalization == "air conditioned floor area":
             data = pd.read_csv(self.locator.get_total_demand())
             normalizatioon_factor = sum(data['Af_m2'])
             data_processed = data_processed.apply(
                 lambda x: x / normalizatioon_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
         elif normalization == "building occupancy":
             data = pd.read_csv(self.locator.get_total_demand())
             normalizatioon_factor = sum(data['people0'])
             data_processed = data_processed.apply(
                 lambda x: x / normalizatioon_factor if x.name in analysis_fields else x)
             data_processed['GHG_sys_district_scale_tonCO2'] = data_processed['GHG_sys_district_scale_tonCO2'] * 1000  # convert to kg
             data_processed['GHG_sys_building_scale_tonCO2'] = data_processed['GHG_sys_building_scale_tonCO2'] * 1000  # convert to kg
-            data_processed['GHG_sys_embodied_tonCO2'] = data_processed['GHG_sys_embodied_tonCO2'] * 1000  # convert to kg
+            data_processed['GHG_sys_embodied_tonCO2yr'] = data_processed['GHG_sys_embodied_tonCO2yr'] * 1000  # convert to kg
 
         return data_processed
 

diff --git a/cea/plots/optimization/d_annual_emissions.py b/cea/plots/optimization/d_annual_emissions.py
@@ -30,7 +30,7 @@ def __init__(self, project, parameters, cache):
         super(AnnualEmissionsPlot, self).__init__(project, parameters, cache)
         self.analysis_fields = ["GHG_sys_district_scale_tonCO2",
                                 "GHG_sys_building_scale_tonCO2",
-                                "GHG_sys_embodied_tonCO2",
+                                "GHG_sys_embodied_tonCO2yr",
                                 ]
         self.normalization = self.parameters['normalization']
         self.input_files = [(self.locator.get_optimization_generation_total_performance_pareto, [self.generation]),

diff --git a/cea/schemas.yml b/cea/schemas.yml
@@ -10635,14 +10635,14 @@ get_lca_embodied:
         unit: '[m2]'
         values: '{0.0...n}'
         min: 0.0
-      GHG_sys_embodied_kgCO2m2:
+      GHG_sys_embodied_kgCO2m2yr:
         description: Embodied emissions per conditioned floor area due to building
           construction and decommissioning
         type: float
         unit: "[kg CO2-eq/m2.yr]"
         values: '{0.0...n}'
         min: 0.0
-      GHG_sys_embodied_tonCO2:
+      GHG_sys_embodied_tonCO2yr:
         description: Embodied emissions due to building construction and decommissioning
         type: float
         unit: "[ton CO2-eq/yr]"