Electrical load-temperature CNN for residential load forecasting

Residential load forecasting is a challenging problem due to complex relations among the hourly electrical load values along the time and also nonlinear relationships among the consumed electricity values and their associated temperature values. A nonlinear relationship extraction (NRE) method is proposed in this work. NRE obtains a load cube where each hourly load value is surrounded by load values of past, present and future hours in previous, same and next days of the same week and previous week. Then, a convolutional neural network (CNN) is used to extract the nonlinear relationships among the load values. In addition, a load-temperature cube is composed from the hourly load and temperature values of a week. Another CNN is trained by using the load-temperature cubes to learn the hidden nonlinear load temperature features. The extracted features are given to a support vector regression (SVR) for load forecasting. The two dimensional convolutional operator is utilized for local feature extraction from the neighborhood regions; the nonlinear activation function is used for nonlinear feature extraction; and the SVR with Gaussian kernel is employed for minimizing the forecasting error. The forecasting results show the superior performance of the proposed method compared to several outstanding forecasters (c) 2021 Elsevier Ltd. All rights reserved.