A Deep Learning model for day-ahead load forecasting taking advantage of expert knowledge

Abstract

Deep learning plays an important role in short-term load forecasting. By combining deep learning model with expert knowledge, the accuracy and interpretation of load forecasting model can be further improved. Under the guidance of load forecasting theory, the effect of holidays on daily load was taken into account. This paper proposes a new convolutional LSTM (ConvLSTM) deep learning model with taking advantage of expert knowledge. Simulation results show that the proposed ConvLSTM network captures spatiotemporal correlations better and consistently outperforms LSTM for load forecasting, especially in holidays load forecasting.