Create energy_forecaster.py

energonexus/core/neural_networks/energy_forecaster.py

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+import pandas as pd
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+
+class EnergyForecaster:
+    def __init__(self):
+        self.model = Sequential()
+        self.model.add(LSTM(units=50, return_sequences=True, input_shape=(10, 1)))
+        self.model.add(LSTM(units=50))
+        self.model.add(Dense(1))
+        self.model.compile(loss='mean_squared_error', optimizer='adam')
+
+    def train(self, data: pd.DataFrame):
+        self.model.fit(data, epochs=100, batch_size=32)
+
+    def forecast(self, future_days: int):
+        future_data = pd.DataFrame(index=range(future_days), columns=['energy_consumption'])
+        for i in range(future_days):
+            input_data = future_data.iloc[i-10:i].values.reshape((1, 10, 1))
+            output = self.model.predict(input_data)
+            future_data.iloc[i, 0] = output[0][0]
+        return future_data
+
+energy_forecaster = EnergyForecaster()