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Moved load_profile db to data
removed comments fixed Bug in visualize.py (extra data empty) removed dead cp
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Andreas
Andreas
parent
ec1dc87104
commit
caf907a2e5
@ -116,7 +116,8 @@ def flask_gesamtlast():
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measured_data["time"] = measured_data["time"].dt.tz_localize(None)
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# Instantiate LoadForecast and generate forecast data
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lf = LoadForecast(filepath=r"load_profiles.npz", year_energy=year_energy)
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file_path = os.path.join("data", "load_profiles.npz")
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lf = LoadForecast(filepath=file_path, year_energy=year_energy)
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forecast_list = []
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# Generate daily forecasts for the date range based on measured data
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@ -153,68 +154,6 @@ def flask_gesamtlast():
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return jsonify(last.tolist())
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# @app.route('/gesamtlast', methods=['GET'])
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# def flask_gesamtlast():
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# if request.method == 'GET':
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# year_energy = float(request.args.get("year_energy")) # Get annual energy value from query parameters
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# prediction_hours = int(request.args.get("hours", 48)) # Default to 48 hours if not specified
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# date_now = datetime.now() # Get the current date and time
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# end_date = (date_now + timedelta(hours=prediction_hours)).strftime('%Y-%m-%d %H:%M:%S') # Calculate end date based on prediction hours
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# ###############
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# # Load Forecast
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# ###############
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# # Instantiate LastEstimator to retrieve measured data
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# estimator = LastEstimator()
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# start_date = (date_now - timedelta(days=60)).strftime('%Y-%m-%d') # Start date: last 60 days
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# end_date = date_now.strftime('%Y-%m-%d') # Current date
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# last_df = estimator.get_last(start_date, end_date) # Get last load data
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# selected_columns = last_df[['timestamp', 'Last']] # Select relevant columns
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# selected_columns['time'] = pd.to_datetime(selected_columns['timestamp']).dt.floor('H') # Floor timestamps to the nearest hour
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# selected_columns['Last'] = pd.to_numeric(selected_columns['Last'], errors='coerce') # Convert 'Last' to numeric, coerce errors
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# cleaned_data = selected_columns.dropna() # Clean data by dropping NaN values
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# # Instantiate LoadForecast
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# lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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# # Generate forecast data
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# forecast_list = [] # List to hold daily forecasts
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# for single_date in pd.date_range(cleaned_data['time'].min().date(), cleaned_data['time'].max().date()): # Iterate over date range
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# date_str = single_date.strftime('%Y-%m-%d') # Format date
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# daily_forecast = lf.get_daily_stats(date_str) # Get daily stats from LoadForecast
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# mean_values = daily_forecast[0] # Extract mean values
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# hours = [single_date + pd.Timedelta(hours=i) for i in range(24)] # Generate hours for the day
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# daily_forecast_df = pd.DataFrame({'time': hours, 'Last Pred': mean_values}) # Create DataFrame for daily forecast
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# forecast_list.append(daily_forecast_df) # Append to the list
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# forecast_df = pd.concat(forecast_list, ignore_index=True) # Concatenate all daily forecasts
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# # Create LoadPredictionAdjuster instance
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# adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
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# adjuster.calculate_weighted_mean() # Calculate weighted mean for adjustments
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# adjuster.adjust_predictions() # Adjust predictions based on measured data
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# # Predict the next hours
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# future_predictions = adjuster.predict_next_hours(prediction_hours) # Predict future load
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# leistung_haushalt = future_predictions['Adjusted Pred'].values # Extract household power predictions
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# gesamtlast = Gesamtlast(prediction_hours=prediction_hours) # Create Gesamtlast instance
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# gesamtlast.hinzufuegen("Haushalt", leistung_haushalt) # Add household load to total load calculation
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# # ###############
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# # # WP (Heat Pump)
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# # ##############
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# # leistung_wp = wp.simulate_24h(temperature_forecast) # Simulate heat pump load for 24 hours
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# # gesamtlast.hinzufuegen("Heatpump", leistung_wp) # Add heat pump load to total load calculation
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# last = gesamtlast.gesamtlast_berechnen() # Calculate total load
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# print(last) # Output total load
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# return jsonify(last.tolist()) # Return total load as JSON
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@app.route("/gesamtlast_simple", methods=["GET"])
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def flask_gesamtlast_simple():
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if request.method == "GET":
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@ -228,8 +167,10 @@ def flask_gesamtlast_simple():
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###############
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# Load Forecast
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###############
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file_path = os.path.join("data", "load_profiles.npz")
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lf = LoadForecast(
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filepath=r"load_profiles.npz", year_energy=year_energy
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filepath=file_path, year_energy=year_energy
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) # Instantiate LoadForecast with specified parameters
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leistung_haushalt = lf.get_stats_for_date_range(date_now, date)[
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0
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@ -101,7 +101,7 @@ class LoadForecast:
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# Example usage of the class
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if __name__ == "__main__":
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filepath = r"..\load_profiles.npz" # Adjust the path to the .npz file
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filepath = r"..\data\load_profiles.npz" # Adjust the path to the .npz file
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lf = LoadForecast(filepath=filepath, year_energy=2000)
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specific_date_prices = lf.get_daily_stats("2024-02-16") # Adjust date as needed
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specific_hour_stats = lf.get_hourly_stats(
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@ -3,17 +3,6 @@ import numpy as np
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import pandas as pd
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from sklearn.metrics import mean_squared_error, r2_score
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# from sklearn.model_selection import train_test_split, GridSearchCV
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# from sklearn.ensemble import GradientBoostingRegressor
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# from xgboost import XGBRegressor
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# from statsmodels.tsa.statespace.sarimax import SARIMAX
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# from tensorflow.keras.models import Sequential
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# from tensorflow.keras.layers import Dense, LSTM
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# from tensorflow.keras.optimizers import Adam
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# from sklearn.preprocessing import MinMaxScaler
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# from sqlalchemy import create_engine
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class LoadPredictionAdjuster:
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def __init__(self, measured_data, predicted_data, load_forecast):
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self.measured_data = measured_data
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@ -338,8 +338,6 @@ class optimization_problem:
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extra_data=extra_data,
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)
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os.system("cp visualisierungsergebnisse.pdf ~/")
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# Return final results as a dictionary
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return {
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"discharge_hours_bin": discharge_hours_bin,
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@ -110,7 +110,7 @@ def visualisiere_ergebnisse(
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plt.figure(figsize=(14, 10))
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if ist_dst_wechsel(datetime.now()):
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if ist_dst_wechsel(datetime.datetime.now()):
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hours = np.arange(start_hour, prediction_hours - 1)
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else:
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hours = np.arange(start_hour, prediction_hours)
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@ -262,31 +262,31 @@ def visualisiere_ergebnisse(
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if n < 0.01
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]
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)
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if filtered_losses.size != 0:
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best_loss = min(filtered_losses)
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worst_loss = max(filtered_losses)
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best_balance = min(filtered_balance)
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worst_balance = max(filtered_balance)
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best_loss = min(filtered_losses)
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worst_loss = max(filtered_losses)
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best_balance = min(filtered_balance)
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worst_balance = max(filtered_balance)
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data = [filtered_losses, filtered_balance]
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labels = ["Losses", "Balance"]
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# Create plots
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fig, axs = plt.subplots(
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1, 2, figsize=(10, 6), sharey=False
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) # Two subplots, separate y-axes
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data = [filtered_losses, filtered_balance]
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labels = ["Losses", "Balance"]
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# Create plots
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fig, axs = plt.subplots(
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1, 2, figsize=(10, 6), sharey=False
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) # Two subplots, separate y-axes
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# First violin plot for losses
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axs[0].violinplot(data[0], showmeans=True, showmedians=True)
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axs[0].set_title("Losses")
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axs[0].set_xticklabels(["Losses"])
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# First violin plot for losses
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axs[0].violinplot(data[0], showmeans=True, showmedians=True)
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axs[0].set_title("Losses")
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axs[0].set_xticklabels(["Losses"])
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# Second violin plot for balance
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axs[1].violinplot(data[1], showmeans=True, showmedians=True)
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axs[1].set_title("Balance")
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axs[1].set_xticklabels(["Balance"])
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# Second violin plot for balance
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axs[1].violinplot(data[1], showmeans=True, showmedians=True)
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axs[1].set_title("Balance")
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axs[1].set_xticklabels(["Balance"])
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# Fine-tuning
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plt.tight_layout()
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# Fine-tuning
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plt.tight_layout()
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pdf.savefig() # Save the current figure state to the PDF
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plt.close() # Close the figure
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