Moved load_profile db to data

removed comments
fixed Bug in visualize.py (extra data empty)
removed dead cp

flask_server.py

@@ -116,7 +116,8 @@ def flask_gesamtlast():
     measured_data["time"] = measured_data["time"].dt.tz_localize(None)
 
     # Instantiate LoadForecast and generate forecast data
-    lf = LoadForecast(filepath=r"load_profiles.npz", year_energy=year_energy)
+    file_path = os.path.join("data", "load_profiles.npz")
+    lf = LoadForecast(filepath=file_path, year_energy=year_energy)
     forecast_list = []
 
     # Generate daily forecasts for the date range based on measured data
@@ -153,68 +154,6 @@ def flask_gesamtlast():
     return jsonify(last.tolist())
 
 
-# @app.route('/gesamtlast', methods=['GET'])
-# def flask_gesamtlast():
-#     if request.method == 'GET':
-#         year_energy = float(request.args.get("year_energy"))  # Get annual energy value from query parameters
-#         prediction_hours = int(request.args.get("hours", 48))  # Default to 48 hours if not specified
-#         date_now = datetime.now()  # Get the current date and time
-#         end_date = (date_now + timedelta(hours=prediction_hours)).strftime('%Y-%m-%d %H:%M:%S')  # Calculate end date based on prediction hours
-
-#         ###############
-#         # Load Forecast
-#         ###############
-#         # Instantiate LastEstimator to retrieve measured data
-#         estimator = LastEstimator()
-#         start_date = (date_now - timedelta(days=60)).strftime('%Y-%m-%d')  # Start date: last 60 days
-#         end_date = date_now.strftime('%Y-%m-%d')  # Current date
-
-#         last_df = estimator.get_last(start_date, end_date)  # Get last load data
-
-#         selected_columns = last_df[['timestamp', 'Last']]  # Select relevant columns
-#         selected_columns['time'] = pd.to_datetime(selected_columns['timestamp']).dt.floor('H')  # Floor timestamps to the nearest hour
-#         selected_columns['Last'] = pd.to_numeric(selected_columns['Last'], errors='coerce')  # Convert 'Last' to numeric, coerce errors
-#         cleaned_data = selected_columns.dropna()  # Clean data by dropping NaN values
-
-#         # Instantiate LoadForecast
-#         lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
-
-#         # Generate forecast data
-#         forecast_list = []  # List to hold daily forecasts
-#         for single_date in pd.date_range(cleaned_data['time'].min().date(), cleaned_data['time'].max().date()):  # Iterate over date range
-#             date_str = single_date.strftime('%Y-%m-%d')  # Format date
-#             daily_forecast = lf.get_daily_stats(date_str)  # Get daily stats from LoadForecast
-#             mean_values = daily_forecast[0]  # Extract mean values
-#             hours = [single_date + pd.Timedelta(hours=i) for i in range(24)]  # Generate hours for the day
-#             daily_forecast_df = pd.DataFrame({'time': hours, 'Last Pred': mean_values})  # Create DataFrame for daily forecast
-#             forecast_list.append(daily_forecast_df)  # Append to the list
-
-#         forecast_df = pd.concat(forecast_list, ignore_index=True)  # Concatenate all daily forecasts
-
-#         # Create LoadPredictionAdjuster instance
-#         adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
-#         adjuster.calculate_weighted_mean()  # Calculate weighted mean for adjustments
-#         adjuster.adjust_predictions()  # Adjust predictions based on measured data
-
-#         # Predict the next hours
-#         future_predictions = adjuster.predict_next_hours(prediction_hours)  # Predict future load
-
-#         leistung_haushalt = future_predictions['Adjusted Pred'].values  # Extract household power predictions
-
-#         gesamtlast = Gesamtlast(prediction_hours=prediction_hours)  # Create Gesamtlast instance
-#         gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)  # Add household load to total load calculation
-
-#         # ###############
-#         # # WP (Heat Pump)
-#         # ##############
-#         # leistung_wp = wp.simulate_24h(temperature_forecast)  # Simulate heat pump load for 24 hours
-#         # gesamtlast.hinzufuegen("Heatpump", leistung_wp)  # Add heat pump load to total load calculation
-
-#         last = gesamtlast.gesamtlast_berechnen()  # Calculate total load
-#         print(last)  # Output total load
-#         return jsonify(last.tolist())  # Return total load as JSON
-
-
 @app.route("/gesamtlast_simple", methods=["GET"])
 def flask_gesamtlast_simple():
     if request.method == "GET":
@@ -228,8 +167,10 @@ def flask_gesamtlast_simple():
         ###############
         # Load Forecast
         ###############
+        file_path = os.path.join("data", "load_profiles.npz")
+
         lf = LoadForecast(
-            filepath=r"load_profiles.npz", year_energy=year_energy
+            filepath=file_path, year_energy=year_energy
         )  # Instantiate LoadForecast with specified parameters
         leistung_haushalt = lf.get_stats_for_date_range(date_now, date)[
             0

modules/class_load.py

@@ -101,7 +101,7 @@ def _convert_to_datetime(self, date_str):
 
 # Example usage of the class
 if __name__ == "__main__":
-    filepath = r"..\load_profiles.npz"  # Adjust the path to the .npz file
+    filepath = r"..\data\load_profiles.npz"  # Adjust the path to the .npz file
     lf = LoadForecast(filepath=filepath, year_energy=2000)
     specific_date_prices = lf.get_daily_stats("2024-02-16")  # Adjust date as needed
     specific_hour_stats = lf.get_hourly_stats(

modules/class_load_corrector.py

@@ -3,17 +3,6 @@
 import pandas as pd
 from sklearn.metrics import mean_squared_error, r2_score
 
-# from sklearn.model_selection import train_test_split, GridSearchCV
-# from sklearn.ensemble import GradientBoostingRegressor
-# from xgboost import XGBRegressor
-# from statsmodels.tsa.statespace.sarimax import SARIMAX
-# from tensorflow.keras.models import Sequential
-# from tensorflow.keras.layers import Dense, LSTM
-# from tensorflow.keras.optimizers import Adam
-# from sklearn.preprocessing import MinMaxScaler
-# from sqlalchemy import create_engine
-
-
 class LoadPredictionAdjuster:
     def __init__(self, measured_data, predicted_data, load_forecast):
         self.measured_data = measured_data

modules/class_optimize.py

@@ -338,8 +338,6 @@ def optimierung_ems(
             extra_data=extra_data,
         )
 
-        os.system("cp visualisierungsergebnisse.pdf ~/")
-
         # Return final results as a dictionary
         return {
             "discharge_hours_bin": discharge_hours_bin,

modules/visualize.py

@@ -110,7 +110,7 @@ def visualisiere_ergebnisse(
 
         plt.figure(figsize=(14, 10))
 
-        if ist_dst_wechsel(datetime.now()):
+        if ist_dst_wechsel(datetime.datetime.now()):
             hours = np.arange(start_hour, prediction_hours - 1)
         else:
             hours = np.arange(start_hour, prediction_hours)
@@ -262,31 +262,31 @@ def visualisiere_ergebnisse(
                     if n < 0.01
                 ]
             )
-
-            best_loss = min(filtered_losses)
-            worst_loss = max(filtered_losses)
-            best_balance = min(filtered_balance)
-            worst_balance = max(filtered_balance)
-
-            data = [filtered_losses, filtered_balance]
-            labels = ["Losses", "Balance"]
-            # Create plots
-            fig, axs = plt.subplots(
-                1, 2, figsize=(10, 6), sharey=False
-            )  # Two subplots, separate y-axes
-
-            # First violin plot for losses
-            axs[0].violinplot(data[0], showmeans=True, showmedians=True)
-            axs[0].set_title("Losses")
-            axs[0].set_xticklabels(["Losses"])
-
-            # Second violin plot for balance
-            axs[1].violinplot(data[1], showmeans=True, showmedians=True)
-            axs[1].set_title("Balance")
-            axs[1].set_xticklabels(["Balance"])
-
-            # Fine-tuning
-            plt.tight_layout()
+            if filtered_losses.size != 0:
+                best_loss = min(filtered_losses)
+                worst_loss = max(filtered_losses)
+                best_balance = min(filtered_balance)
+                worst_balance = max(filtered_balance)
+
+                data = [filtered_losses, filtered_balance]
+                labels = ["Losses", "Balance"]
+                # Create plots
+                fig, axs = plt.subplots(
+                    1, 2, figsize=(10, 6), sharey=False
+                )  # Two subplots, separate y-axes
+
+                # First violin plot for losses
+                axs[0].violinplot(data[0], showmeans=True, showmedians=True)
+                axs[0].set_title("Losses")
+                axs[0].set_xticklabels(["Losses"])
+
+                # Second violin plot for balance
+                axs[1].violinplot(data[1], showmeans=True, showmedians=True)
+                axs[1].set_title("Balance")
+                axs[1].set_xticklabels(["Balance"])
+
+                # Fine-tuning
+                plt.tight_layout()
 
             pdf.savefig()  # Save the current figure state to the PDF
             plt.close()  # Close the figure