Added KPIs and tests for EnergyGrid

src/simstadt/results/energy_grid.py

@@ -2,6 +2,7 @@ import json
 from dataclasses import dataclass
 from typing import ClassVar
 
+import numpy as np
 import pandas as pd
 
 from .base import SimStadtResults
@@ -22,16 +23,30 @@ class EnergyGridResults(SimStadtResults):
         json_output = jsons[0]
         with open(json_output, encoding="utf-8") as out:
             content = json.load(out)
-        # TODO: Add infos about buildings. Total consumption for example?
-        return pd.DataFrame(
-            {
-                "ambient_temperature": content["ambient_temperature"],
-            }
-        )
+        buildings = content["buildings"]
+        df = pd.DataFrame({
+            "ambient_temperature": content["ambient_temperature"],
+            "electricity_demand": np.sum([b["building_electricity_demand"] for b in buildings], axis=0),
+            "heat_demand": np.sum([b["th_heat_demand"] for b in buildings], axis=0),
+            "pv_production": np.sum([b["pv_production"] for b in buildings], axis=0),
+        })
+        df.attrs["number_of_buildings"] = len(buildings)
+        df.attrs["total_heated_area"] = sum(b["heated_area"] for b in buildings)
+        return df
 
     @property
     def kpis(self) -> list[KPI]:
         df = self.dataframe
+        total_heated_area = df.attrs["total_heated_area"]
+        total_heat_kwh = df["heat_demand"].sum()
+        total_elec_kwh = df["electricity_demand"].sum()
         return [
+            KPI("Number of buildings", df.attrs["number_of_buildings"]),
+            KPI("Total heated area", total_heated_area, "m²", precision=0),
+            KPI("Total electricity demand", total_elec_kwh / 1000, "MWh / a", precision=1),
+            KPI("Specific electricity demand", total_elec_kwh / total_heated_area, "kWh / (m² · a)", precision=1),
+            KPI("Total heat demand", total_heat_kwh / 1000, "MWh / a", precision=1),
+            KPI("Specific heat demand", total_heat_kwh / total_heated_area, "kWh / (m² · a)", precision=1),
+            KPI("Total PV production", df["pv_production"].sum() / 1000, "MWh / a", precision=1),
             KPI("Average temperature", float(df["ambient_temperature"].mean()), "°C", precision=1),
         ]

tests/test_results.py

@@ -644,13 +644,30 @@ def test_energy_grid_output_files_exist(energy_grid_results):
 def test_energy_grid_dataframe(energy_grid_results):
     df = energy_grid_results.dataframe
     assert isinstance(df, pd.DataFrame)
-    assert df.shape == (8760, 1)
-    assert list(df.columns) == ["ambient_temperature"]
+    assert df.shape == (8760, 4)
+    assert list(df.columns) == ["ambient_temperature", "electricity_demand", "heat_demand", "pv_production"]
+    assert df.attrs["number_of_buildings"] == 3
+
 
 def test_energy_grid_kpis(energy_grid_results):
     kpis = kpis_by_name(energy_grid_results)
+    assert kpis["Number of buildings"].value == 3
+    assert kpis["Number of buildings"].unit is None
+    assert kpis["Total heated area"].value == pytest.approx(1198.1, abs=0.1)
+    assert kpis["Total heated area"].unit == "m²"
+    assert kpis["Total electricity demand"].value == pytest.approx(17.0, abs=0.1)
+    assert kpis["Total electricity demand"].unit == "MWh / a"
+    assert kpis["Specific electricity demand"].value == pytest.approx(14.2, abs=0.5)
+    assert kpis["Specific electricity demand"].unit == "kWh / (m² · a)"
+    assert kpis["Total heat demand"].value == pytest.approx(117.9, abs=0.1)
+    assert kpis["Total heat demand"].unit == "MWh / a"
+    assert kpis["Specific heat demand"].value == pytest.approx(98.4, abs=0.5)
+    assert kpis["Specific heat demand"].unit == "kWh / (m² · a)"
+    assert kpis["Total PV production"].value == pytest.approx(34.5, abs=0.1)
+    assert kpis["Total PV production"].unit == "MWh / a"
     assert kpis["Average temperature"].value == pytest.approx(9.95, abs=0.05)
     assert kpis["Average temperature"].unit == "°C"
+    assert len(kpis) == 8
 
 
 # ---------------------------------------------------------------------------