Convolutional residual network to short-term load forecasting

Since their inception, convolutional neural networks (CNNs) have been shown to have powerful feature extraction and learning capabilities, and the creation of deep residual networks (DRNs) was a milestone in the development of CNNs. However, residual networks mostly use convolution structures, which are widely applied to image recognition and classification problems. Therefore, when facing a load forecasting problem that involves nonlinear regression, will a DRN using a convolution structure still achieve great results? To answer this question, we present a network based on a DRN with a convolution structure to carry out short-term load forecasting, and we mainly focus on the effects of DRNs with different depths, widths and block structures for dealing with nonlinear regression problems. Through multiple sets of controlled experiments, we obtain the best network architecture and the corresponding hyperparameters for short-term load forecasting. The experimental results demonstrate that the model has higher prediction accuracy than existing models, and the DRN with a convolution structure can handle load forecasting while still achieving state-of-the-art results.