Deep learning-based short-term load forecasting for supporting demand response program in hybrid energy system

Abstract

A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2.