Hybrid Deep Learning Method Based on LSTM-Autoencoder Network for Household Short-term Load Forecasting

Energy prediction is an essential task in smart homes for demand-side management and energy consumption reduction. Therefore, an intelligent forecasting model is necessary for predicting demand-side energy in residential buildings. Recent studies have shown that deep learning networks have higher performance than traditional machine learning methods in short-term load forecasting. In this paper, a new hybrid network is proposed that consists of Auto-Encoder LSTM layer, Bi-LSTM layer, stack of LSTM layer, and finally Fully connected layer. The experiments are conducted on an individual household electric power consumption dataset and the results demonstrate that the proposed network has the smallest value in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in comparison with other state-of-the-art approaches.