Merge pull request #418 from openego/bugfix/update_demandlib

Bugfix/update demandlib

edisgo/config/config_timeseries_default.cfg

@@ -129,6 +129,8 @@ week_day = 0.8
 week_night = 0.6
 weekend_day = 0.6
 weekend_night = 0.6
+holiday_day = 0.6
+holiday_night = 0.6
 # tuple specifying the beginning/end of a workday (e.g. 18:00)
 day_start = 6:00
 day_end = 22:00

edisgo/io/timeseries_import.py

@@ -297,6 +297,10 @@ def load_time_series_demandlib(edisgo_obj, timeindex=None):
                 "day": edisgo_obj.config["demandlib"]["weekend_day"],
                 "night": edisgo_obj.config["demandlib"]["weekend_night"],
             },
+            "holiday": {
+                "day": edisgo_obj.config["demandlib"]["holiday_day"],
+                "night": edisgo_obj.config["demandlib"]["holiday_night"],
+            },
         },
     )
 

rtd_requirements.txt

@@ -1,5 +1,5 @@
 dash < 2.9.0
-demandlib < 0.2.0
+demandlib
 egoio >= 0.4.7
 geopy >= 2.0.0
 jupyter_dash

setup.py

@@ -33,7 +33,7 @@ def read(fname):
 requirements = [
     "contextily",
     "dash < 2.9.0",
-    "demandlib < 0.2.0",
+    "demandlib",
     "descartes",
     "egoio >= 0.4.7",
     "geoalchemy2 < 0.7.0",

tests/io/test_timeseries_import.py

@@ -89,16 +89,20 @@ def test_feedin_oedb(self):
 
     def test_load_time_series_demandlib(self):
         edisgo = EDisGo(ding0_grid=pytest.ding0_test_network_path)
-        timeindex = pd.date_range("1/1/2018", periods=7000, freq="H")
+        timeindex = pd.date_range("1/1/2018", periods=8760, freq="H")
         load = timeseries_import.load_time_series_demandlib(edisgo, timeindex)
         assert (
             load.columns == ["cts", "residential", "agricultural", "industrial"]
         ).all()
-        assert len(load) == 7000
+        assert len(load) == 8760
         assert np.isclose(load.loc[timeindex[453], "cts"], 8.33507e-05)
         assert np.isclose(load.loc[timeindex[13], "residential"], 1.73151e-04)
         assert np.isclose(load.loc[timeindex[6328], "agricultural"], 1.01346e-04)
-        assert np.isclose(load.loc[timeindex[4325], "industrial"], 9.91768e-05)
+        assert np.isclose(load.loc[timeindex[4325], "industrial"], 9.87654320e-05)
+        assert np.isclose(load.sum()["cts"], 1.0)
+        assert np.isclose(load.sum()["residential"], 1.0)
+        assert np.isclose(load.sum()["agricultural"], 1.0)
+        assert np.isclose(load.sum()["industrial"], 1.0)
 
     @pytest.mark.local
     def test_cop_oedb(self):

tests/network/test_timeseries.py

@@ -1565,9 +1565,9 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
             index=index,
             columns=["cts", "residential", "agricultural", "industrial"],
             data=[
-                [0.0000597, 0.0000782, 0.0000654, 0.0000992],
-                [0.0000526, 0.0000563, 0.0000611, 0.0000992],
-                [0.0000459, 0.0000451, 0.0000585, 0.0000992],
+                [0.000059711, 0.0000782190, 0.00006540, 0.00009876],
+                [0.000052590, 0.0000563428, 0.00006110, 0.00009876],
+                [0.000045927, 0.0000451043, 0.00005843, 0.00009876],
             ],
         )
 
@@ -1656,7 +1656,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
             self.edisgo.timeseries.loads_active_power[
                 "Load_industrial_LVGrid_6_1"
             ].values,
-            [0.05752256] * 3,
+            [0.05728395] * 3,
         ).all()
         assert np.isclose(
             self.edisgo.timeseries.loads_active_power.loc[