Merge pull request #782 from openego/fixes/#781-fix-heat-demand-time-…

…series

Fixes/#781 fix heat demand time series

CHANGELOG.rst

@@ -550,6 +550,8 @@ Bug Fixes
   `#776 <https://github.com/openego/eGon-data/issues/776>`_
 * Fix model load timeseries in motorized individual travel
   `#830 <https://github.com/openego/eGon-data/issues/830>`_
+* Fix final demand of heat demand timeseries
+  `#781 <https://github.com/openego/eGon-data/issues/781>`_
 
 .. _PR #692: https://github.com/openego/eGon-data/pull/692
 .. _#343: https://github.com/openego/eGon-data/issues/343

src/egon/data/datasets/heat_demand_timeseries/HTS.py

@@ -999,14 +999,14 @@ def profile_generator(aggregation_level):
 
     heat_profile_dist : pandas.DataFrame
         if aggreation_level = 'district'
-            heat profiles for every distric heating id
+            normalized heat profiles for every distric heating id
         else
             0
     heat_profile_idp : pandas.DataFrame
         if aggreation_level = 'district'
-            heat profiles for every mv grid bus_id
+            normalized heat profiles for every mv grid bus_id
         else
-            heat profiles for every zensus_poppulation_id
+            normalized heat profiles for every zensus_poppulation_id
     """
 
     annual_demand, idp_df, selected_profiles = profile_selector()
@@ -1077,6 +1077,7 @@ def profile_generator(aggregation_level):
                 right_on=mv_grid_ind.index,
                 how="inner",
             )
+
             heat_profile_idp.sort_values("bus_id", inplace=True)
             heat_profile_idp.set_index("bus_id", inplace=True)
             heat_profile_idp.drop("key_0", axis=1, inplace=True)
@@ -1160,28 +1161,34 @@ def residential_demand_scale(aggregation_level):
     h = h_value()
     h = h.reset_index(drop=True)
 
-    district_heating = psycop_df_AF(
-        "demand.egon_map_zensus_district_heating_areas"
-    )
-
-    district_heating = district_heating.pivot_table(
-        values="area_id", index="zensus_population_id", columns="scenario"
-    )
-
     mv_grid = psycop_df_AF("boundaries.egon_map_zensus_grid_districts")
 
-    mv_grid_ind = mv_grid.loc[
-        mv_grid.index.difference(district_heating.index), :
-    ]
-    mv_grid_ind = mv_grid_ind.reset_index()
-
     scenarios = ["eGon2035", "eGon100RE"]
 
     residential_dist_profile = pd.DataFrame()
     residential_individual_profile = pd.DataFrame()
     residential_zensus_profile = pd.DataFrame()
 
     for scenario in scenarios:
+
+        district_heating = db.select_dataframe(
+            f"""
+            SELECT * FROM 
+            demand.egon_map_zensus_district_heating_areas
+            WHERE scenario = '{scenario}'
+            """
+        )
+
+        district_heating = district_heating.pivot_table(
+            values="area_id", index="zensus_population_id", columns="scenario"
+        )
+
+        mv_grid_ind = mv_grid[
+            ~mv_grid.zensus_population_id.isin(district_heating.index)
+        ]
+
+        mv_grid_ind = mv_grid_ind.reset_index()
+
         if aggregation_level == "district":
 
             scenario_ids = district_heating[scenario]
@@ -1265,6 +1272,7 @@ def residential_demand_scale(aggregation_level):
                 heat_demand_profile_dist
             )
 
+            # Individual supplied heat demand time series
             district_grid = pd.merge(
                 mv_grid_ind[["bus_id", "zensus_population_id"]],
                 annual_demand[["zensus_population_id", "Station", scenario]],
@@ -1797,10 +1805,60 @@ def demand_profile_generator(aggregation_level="district"):
         residential_demand_zensus,
     ) = residential_demand_scale(aggregation_level)
 
+    # Compare with target value
+    target = db.select_dataframe(
+        """
+        SELECT scenario, SUM(demand) as demand
+        FROM demand.egon_peta_heat
+        WHERE sector = 'residential'
+        GROUP BY (scenario)
+        """,
+        index_col="scenario",
+    )
+
+    check_residential = (
+        (
+            residential_demand_dist.groupby("scenario").sum().sum(axis=1)
+            + residential_demand_grid.groupby("scenario").sum().sum(axis=1)
+        )
+        - target.demand
+    ) / target.demand
+
+    assert (
+        check_residential.abs().max() < 0.01
+    ), f"""Unexpected deviation between target value and distributed
+        residential heat demand: {check_residential}
+        """
+
     CTS_demand_dist, CTS_demand_grid, CTS_demand_zensus = CTS_demand_scale(
         aggregation_level
     )
 
+    # Compare with target value
+    target_cts = db.select_dataframe(
+        """
+        SELECT scenario, SUM(demand) as demand
+        FROM demand.egon_peta_heat
+        WHERE sector = 'service'
+        GROUP BY (scenario)
+        """,
+        index_col="scenario",
+    )
+
+    check_cts = (
+        (
+            CTS_demand_dist.groupby("scenario").sum().sum(axis=1)
+            + CTS_demand_grid.groupby("scenario").sum().sum(axis=1)
+        )
+        - target_cts.demand
+    ) / target_cts.demand
+
+    assert (
+        check_cts.abs().max() < 0.01
+    ), f"""Unexpected deviation between target value and distributed
+        service heat demand: {check_residential}
+        """
+
     # store demand timeseries for pypsa-eur-sec on national level
     store_national_profiles(
         residential_demand_grid,
@@ -1809,6 +1867,7 @@ def demand_profile_generator(aggregation_level="district"):
         CTS_demand_dist,
     )
 
+
     if aggregation_level == "district":
         total_demands_dist = pd.concat(
             [residential_demand_dist, CTS_demand_dist]
@@ -1923,7 +1982,7 @@ class HeatTimeSeries(Dataset):
     def __init__(self, dependencies):
         super().__init__(
             name="HeatTimeSeries",
-            version="0.0.6",
+            version="0.0.7",
             dependencies=dependencies,
             tasks=(demand_profile_generator),
         )