Normalize peak power only for peak_tot, not for peak_dict.

ibpsa · Jan 11, 2024 · 08a4dfc · 08a4dfc
1 parent f032705
commit 08a4dfc
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/kpis/kpi_calculator.py b/kpis/kpi_calculator.py
@@ -412,10 +412,10 @@ def get_peak_electricity(self):
                     df_pow_data_all = pd.concat([df_pow_data_all, df_pow_data], axis=1)
             df_pow_data_all.index = pd.TimedeltaIndex(df_pow_data_all.index, unit='s')
             df_pow_data_all['total_demand'] = df_pow_data_all.sum(axis=1)
-            df_pow_data_all = df_pow_data_all.resample('15T').mean()/self.case._get_area()/1000.
+            df_pow_data_all = df_pow_data_all.resample('15T').mean()/1000.
             i = df_pow_data_all['total_demand'].idxmax()
             peak = df_pow_data_all.loc[i,'total_demand']
-            self.pele_tot = peak
+            self.pele_tot = peak/self.case._get_area()
             # Find contributions to peak by each signal
             for signal in self.case.kpi_json[source]:
                 self.pele_dict[signal] = df_pow_data_all.loc[i,signal]
@@ -459,10 +459,10 @@ def get_peak_gas(self):
                     df_pow_data_all = pd.concat([df_pow_data_all, df_pow_data], axis=1)
             df_pow_data_all.index = pd.TimedeltaIndex(df_pow_data_all.index, unit='s')
             df_pow_data_all['total_demand'] = df_pow_data_all.sum(axis=1)
-            df_pow_data_all = df_pow_data_all.resample('15T').mean()/self.case._get_area()/1000.
+            df_pow_data_all = df_pow_data_all.resample('15T').mean()/1000.
             i = df_pow_data_all['total_demand'].idxmax()
             peak = df_pow_data_all.loc[i,'total_demand']
-            self.pgas_tot = peak
+            self.pgas_tot = peak/self.case._get_area()
             # Find contributions to peak by each signal
             for signal in self.case.kpi_json[source]:
                 self.pgas_dict[signal] = df_pow_data_all.loc[i,signal]
@@ -506,10 +506,10 @@ def get_peak_district_heating(self):
                     df_pow_data_all = pd.concat([df_pow_data_all, df_pow_data], axis=1)
             df_pow_data_all.index = pd.TimedeltaIndex(df_pow_data_all.index, unit='s')
             df_pow_data_all['total_demand'] = df_pow_data_all.sum(axis=1)
-            df_pow_data_all = df_pow_data_all.resample('15T').mean()/self.case._get_area()/1000.
+            df_pow_data_all = df_pow_data_all.resample('15T').mean()/1000.
             i = df_pow_data_all['total_demand'].idxmax()
             peak = df_pow_data_all.loc[i,'total_demand']
-            self.pdih_tot = peak
+            self.pdih_tot = peak/self.case._get_area()
             # Find contributions to peak by each signal
             for signal in self.case.kpi_json[source]:
                 self.pdih_dict[signal] = df_pow_data_all.loc[i,signal]