Create train_lstm_model.py

train_lstm_model.py

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+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from tensorflow.keras.optimizers import Adam
+
+# Load energy generation dataset (ensure 'energy_data.csv' is in the /data folder)
+data = pd.read_csv('data/energy_data.csv')  # Replace with your actual filename if different
+energy_data = data['energy_generation'].values.reshape(-1, 1)
+
+# Normalize data
+scaler = MinMaxScaler(feature_range=(0, 1))
+energy_data = scaler.fit_transform(energy_data)
+
+# Prepare data for LSTM
+X, y = [], []
+for i in range(60, len(energy_data)):
+    X.append(energy_data[i-60:i, 0])
+    y.append(energy_data[i, 0])
+X, y = np.array(X), np.array(y)
+X = X.reshape((X.shape[0], X.shape[1], 1))
+
+# Define LSTM model
+model = Sequential()
+model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
+model.add(LSTM(units=50, return_sequences=False))
+model.add(Dense(units=1))
+
+# Compile and train
+model.compile(optimizer=Adam(), loss='mean_squared_error')
+model.fit(X, y, epochs=10, batch_size=32)
+
+# Save trained model
+model.save('models/lstm_energy_model.h5')
+print("✅ LSTM model trained and saved to models/lstm_energy_model.h5")