EMD-Att-LSTM: A Data-driven Strategy Combined with Deep Learning for Short-term Load Forecasting

Abstract

Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, nonlinear, and noisy nature of the observed data. Therefore, a new attention-based encoder-decoder model is proposed, called empirical mode decomposition-attention-long short-term memory (EMD-Att-LSTM). EMD is a data-driven technique used for the decomposition of complex series into subsequent simpler series. It explores the inherent properties of data to obtain the components such as trend and seasonality. Neural network architecture driven by deep learning uses the idea of a fine-grained attention mechanism, that is, considering the hidden state instead of the hidden state vectors, which can help reflect the significance and contributions of each hidden state dimension. In addition, it is useful for locating and concentrating the relevant temporary data, leading to a distinctly interpretable network. To evaluate the proposed model, we use the repository dataset of Australian energy market operator (AEMO). The proposed architecture provides superior empirical results compared with other advanced models. It is explored using the indices of root mean square error (RMSE) and mean absolute percentage error (MAPE).