Hybrid CNN-LSTM and TimeGAN Models for Enhanced Energy Consumption Prediction in Smart Homes

Accurate prediction of energy consumption in smart homes is essential for optimizing energy usage, reducing operational costs, and enhancing occupant comfort. Traditional statistical and machine learning models often struggle to capture the complex spatiotemporal patterns inherent in energy usage data. To address this limitation, this study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model for forecasting energy consumption in smart homes, combining the spatial feature extraction strengths of Convolutional Neural Networks (CNNs) with the temporal learning capabilities of Long Short-Term Memory (LSTM) networks. The model demonstrated high predictive accuracy and generalization, as reflected by the evaluation metrics: Mean Absolute Error (MAE) = 0.0325, Mean Squared Error (MSE) = 0.0031, Root Mean Squared Error (RMSE) = 0.0557, and Mean Absolute Percentage Error (MAPE) = 0.2054. However, as the model was trained on synthetic data, further validation using real-world energy consumption datasets is necessary to assess its practical applicability and robustness.