Create energy_forecaster.py

energonexus/core/neural_network/energy_forecaster.py

@@ -0,0 +1,22 @@
+import pandas as pd
+import numpy as np
+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=(30, 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):
+        X = data.drop(['energy_consumption'], axis=1)
+        y = data['energy_consumption']
+        self.model.fit(X, y, epochs=100, batch_size=32)
+
+    def predict(self, data: pd.DataFrame):
+        return self.model.predict(data)
+
+energy_forecaster = EnergyForecaster()