Merge branch 'davidusb-geek:master' into weather

Author: paulhomes

pyproject.toml

@@ -7,12 +7,12 @@ build-backend = "hatchling.build"
 [tool.uv.workspace]
 members = ["emhass"]
 
-[tool.hatch.build.targets.wheel]
-packages = ["src/emhass"]
+[tool.uv]
+default-groups = "all"
 
 [project]
 name = "emhass"
-version = "0.13.0"
+version = "0.13.0.post1"
 description = "An Energy Management System for Home Assistant"
 readme = "README.md"
 requires-python = ">=3.10, <3.13"
@@ -51,6 +51,9 @@ docs = ["sphinx", "sphinx-rtd-theme", "myst-parser"]
 # can add "mypy"
 test = ["requests_mock", "pytest", "coverage", "snakeviz", "ruff", "tabulate", "hatchling"]
 
+[tool.hatch.build.targets.wheel]
+packages = ["src/emhass"]
+
 [tool.hatch.build.targets.sdist]
 packages = ["src/emhass"]
 include = [

src/emhass/command_line.py

@@ -69,6 +69,7 @@ def retrieve_home_assistant_data(
         var_list = [retrieve_hass_conf["sensor_power_load_no_var_loads"]]
         if optim_conf.get("set_use_pv", True):
             var_list.append(retrieve_hass_conf["sensor_power_photovoltaics"])
+            var_list.append(retrieve_hass_conf["sensor_power_photovoltaics_forecast"])
         if not rh.get_data(days_list, var_list, minimal_response=False, significant_changes_only=False):
             return False, None, days_list
     rh.prepare_data(

src/emhass/data/cec_inverters.pbz2

@@ -154,6 +154,7 @@ def __init__(
         self.timeStep = self.freq.seconds / 3600  # in hours
         self.time_delta = pd.to_timedelta(opt_time_delta, "hours")
         self.var_PV = self.retrieve_hass_conf["sensor_power_photovoltaics"]
+        self.var_PV_forecast = self.retrieve_hass_conf["sensor_power_photovoltaics_forecast"]
         self.var_load = self.retrieve_hass_conf["sensor_power_load_no_var_loads"]
         self.var_load_new = self.var_load + "_positive"
         self.lat = self.retrieve_hass_conf["Latitude"]
@@ -811,8 +812,11 @@ def adjust_pv_forecast_data_prep(
         :type data: pd.DataFrame
         """
         # Extract target and predictor
-        P_PV = data["sensor.power_photovoltaics"]  # Actual PV production
-        P_PV_forecast = data["sensor.p_pv_forecast"]  # Forecasted PV production
+        self.logger.debug("adjust_pv_forecast_data_prep using data:\n%s", data)
+        if self.logger.isEnabledFor(logging.DEBUG):
+            data.to_csv(self.emhass_conf["data_path"] / 'debug-adjust-pv-forecast-data-prep-input-data.csv')
+        P_PV = data[self.var_PV]  # Actual PV production
+        P_PV_forecast = data[self.var_PV_forecast] # Forecasted PV production
         # Define time ranges
         last_day = data.index.max().normalize()  # Last available day
         three_months_ago = last_day - pd.DateOffset(days=self.retrieve_hass_conf["historic_days_to_retrieve"])
@@ -832,6 +836,9 @@ def adjust_pv_forecast_data_prep(
         # Features (X) and target (y)
         self.X_adjust_pv = self.data_adjust_pv.drop(columns=["actual"])  # Predictors
         self.y_adjust_pv = self.data_adjust_pv["actual"]  # Target: actual PV production
+        self.logger.debug("adjust_pv_forecast_data_prep output data:\n%s", self.data_adjust_pv)
+        if self.logger.isEnabledFor(logging.DEBUG):
+            self.data_adjust_pv.to_csv(self.emhass_conf["data_path"] / 'debug-adjust-pv-forecast-data-prep-output-data.csv')
 
     def adjust_pv_forecast_fit(
         self, n_splits: int = 5, regression_model: str = "LassoRegression", 
@@ -935,6 +942,9 @@ def apply_weighting(row):
             self.validation_r2 = r2_score(y_true, y_pred)
             # Log the validation metrics
             self.logger.info(f"PV adjust Validation metrics: RMSE = {self.validation_rmse}, R2 = {self.validation_r2}")
+        self.logger.debug("adjust_pv_forecast_predict forecast data:\n%s", forecast_data)
+        if self.logger.isEnabledFor(logging.DEBUG):
+            forecast_data.to_csv(self.emhass_conf["data_path"] / 'debug-adjust-pv-forecast-predict-forecast-data.csv')
         # Return the DataFrame with the adjusted forecast
         return forecast_data
 
@@ -1482,8 +1492,11 @@ def get_load_cost_forecast(
             # Ensure correct length
             if not list_and_perfect:
                 forecast_out = forecast_out[0 : len(self.forecast_dates)]
-                df_final = df_final[0 : len(self.forecast_dates)]
-            df_final.loc[:,self.var_load_cost] = forecast_out.values
+                df_final = df_final[0 : len(self.forecast_dates)].copy()
+            # Convert to Series if needed and align index
+            if not isinstance(forecast_out, pd.Series):
+                forecast_out = pd.Series(np.ravel(forecast_out), index=df_final.index)
+            df_final.loc[:, self.var_load_cost] = forecast_out
         elif method == "list":  # reading a list of values
             # Loading data from passed list
             data_list = self.params["passed_data"]["load_cost_forecast"]
@@ -1554,8 +1567,11 @@ def get_prod_price_forecast(
             # Ensure correct length
             if not list_and_perfect:
                 forecast_out = forecast_out[0 : len(self.forecast_dates)]
-                df_final = df_final[0 : len(self.forecast_dates)]
-            df_final.loc[:,self.var_prod_price] = forecast_out.values
+                df_final = df_final[0 : len(self.forecast_dates)].copy()
+            # Convert to Series if needed and align index
+            if not isinstance(forecast_out, pd.Series):
+                forecast_out = pd.Series(np.ravel(forecast_out), index=df_final.index)
+            df_final.loc[:, self.var_prod_price] = forecast_out
         elif method == "list":  # reading a list of values
             # Loading data from passed list
             data_list = self.params["passed_data"]["prod_price_forecast"]