Fix - Fixed variables names problem fir mixed forecasts and prepared …

…new version

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## [0.3.17] - 2022-06-12
+### Fix
+- Fixed wrong variables names for mixed forecasts.
+
 ## [0.3.16] - 2022-06-10
 ### Improvement
 - Improving documentation, added "what is this" section and added some infographics.
@@ -13,7 +17,7 @@
 
 ## [0.3.14] - 2022-06-05
 ### Improvement
-- Added one more table to the weu showing the cost totals.
+- Added one more table to the webui showing the cost totals.
 ### Fix
 - Fixed wrong type error when serializing numpy ints. Converted ints to Python type.
 

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'David HERNANDEZ'
 
 # The full version, including alpha/beta/rc tags
-release = '0.3.16'
+release = '0.3.17'
 
 # -- General configuration ---------------------------------------------------
 

setup.py

@@ -19,7 +19,7 @@
 
 setup(
     name='emhass',  # Required
-    version='0.3.16',  # Required
+    version='0.3.17',  # Required
     description='An Energy Management System for Home Assistant',  # Optional
     long_description=long_description,  # Optional
     long_description_content_type='text/markdown',  # Optional (see note above)

src/emhass/forecast.py

@@ -350,8 +350,7 @@ def get_power_from_weather(self, df_weather: pd.DataFrame,
         if set_mix_forecast:
             P_PV_forecast = forecast.get_mix_forecast(
                 df_now, P_PV_forecast, 
-                self.params['passed_data']['alpha'], self.params['passed_data']['beta'], 
-                'sensor.power_photovoltaics')
+                self.params['passed_data']['alpha'], self.params['passed_data']['beta'], self.var_PV)
 
         return P_PV_forecast
 
@@ -537,8 +536,7 @@ def get_load_forecast(self, days_min_load_forecast: Optional[int] = 3, method: O
         if set_mix_forecast:
             P_Load_forecast = forecast.get_mix_forecast(
                 df_now, P_Load_forecast, 
-                self.params['passed_data']['alpha'], self.params['passed_data']['beta'], 
-                'sensor.power_load_no_var_loads')
+                self.params['passed_data']['alpha'], self.params['passed_data']['beta'], self.var_load_new)
 
         return P_Load_forecast
 

tests/test_forecast.py

@@ -188,6 +188,18 @@ def test_get_power_from_weather(self):
         self.assertIsInstance(P_PV_forecast.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
         self.assertEqual(P_PV_forecast.index.tz, self.fcst.time_zone)
         self.assertEqual(len(self.df_weather_scrap), len(P_PV_forecast))
+        # Test the mixed forecast
+        params = json.dumps({'passed_data':{'alpha':0.5,'beta':0.5}})
+        df_input_data = self.input_data_dict['rh'].df_final.copy()
+        self.fcst = forecast(self.retrieve_hass_conf, self.optim_conf, self.plant_conf, 
+                             params, root, logger, get_data_from_file=self.get_data_from_file)
+        df_weather_scrap = self.fcst.get_weather_forecast(method='scrapper')
+        P_PV_forecast = self.fcst.get_power_from_weather(df_weather_scrap, set_mix_forecast=True, df_now=df_input_data)
+        self.assertIsInstance(P_PV_forecast, pd.core.series.Series)
+        self.assertIsInstance(P_PV_forecast.index, pd.core.indexes.datetimes.DatetimeIndex)
+        self.assertIsInstance(P_PV_forecast.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
+        self.assertEqual(P_PV_forecast.index.tz, self.fcst.time_zone)
+        self.assertEqual(len(self.df_weather_scrap), len(P_PV_forecast))
 
     def test_get_load_forecast(self):
         self.P_load_forecast = self.fcst.get_load_forecast()
@@ -197,6 +209,17 @@ def test_get_load_forecast(self):
         self.assertEqual(self.P_load_forecast.index.tz, self.fcst.time_zone)
         self.assertEqual(len(self.P_PV_forecast), len(self.P_load_forecast))
         print(">> The length of the load forecast = "+str(len(self.P_load_forecast)))
+        # Test the mixed forecast
+        params = json.dumps({'passed_data':{'alpha':0.5,'beta':0.5}})
+        df_input_data = self.input_data_dict['rh'].df_final.copy()
+        self.fcst = forecast(self.retrieve_hass_conf, self.optim_conf, self.plant_conf, 
+                             params, root, logger, get_data_from_file=self.get_data_from_file)
+        self.P_load_forecast = self.fcst.get_load_forecast(set_mix_forecast=True, df_now=df_input_data)
+        self.assertIsInstance(self.P_load_forecast, pd.core.series.Series)
+        self.assertIsInstance(self.P_load_forecast.index, pd.core.indexes.datetimes.DatetimeIndex)
+        self.assertIsInstance(self.P_load_forecast.index.dtype, pd.core.dtypes.dtypes.DatetimeTZDtype)
+        self.assertEqual(self.P_load_forecast.index.tz, self.fcst.time_zone)
+        self.assertEqual(len(self.P_PV_forecast), len(self.P_load_forecast))
 
     def test_get_load_cost_forecast(self):
         df_input_data = self.fcst.get_load_cost_forecast(self.df_input_data)