models/energy_anomaly_detection.py

from tensorflow.keras.models import load_model
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler

# Correct path to your saved LSTM model
model = load_model("models/lstm_energy_model.h5")  # Adjust the path to the actual model location

def detect_energy_anomalies(file_path):
    """
    Detect anomalies in energy generation data (time-series) using LSTM.
    Args:
    - file_path (str): Path to the CSV/JSON file containing energy generation data

    Returns:
    - anomaly (str): Energy anomaly detection result
    """
    # Load energy generation data (assumes CSV format with timestamp and generation columns)
    data = pd.read_csv(file_path)
    energy_data = data['energy_generation'].values.reshape(-1, 1)

    # Normalize data for LSTM
    scaler = MinMaxScaler(feature_range=(0, 1))
    energy_data = scaler.fit_transform(energy_data)

    # Prepare data for LSTM
    X = []
    for i in range(60, len(energy_data)):
        X.append(energy_data[i-60:i, 0])
    X = np.array(X)
    X = X.reshape((X.shape[0], X.shape[1], 1))

    # Predict with LSTM model
    prediction = model.predict(X)
    last_predicted_value = prediction[-1][0]  # Get last prediction
    
    # Define threshold for anomaly (e.g., 10% deviation from the normal value)
    threshold = 0.1
    anomaly = "Anomaly Detected" if abs(last_predicted_value - energy_data[-1][0]) > threshold else "No Anomaly"

    return anomaly