Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression

Abstract

Accurate short-term load forecasting (STLF) is essential to improve secure and economic operation of power systems. In this paper, a hybrid STLF model using the conditional generative adversarial network (CGAN) with convolutional neural network (CNN) and semi-supervised regression is proposed to improve the accuracy of STLF. Firstly, a conditional label matrix with relevant factors is constructed as the conditional labels of CGAN. The grey weighted correlation method is applied to generate high-quality similar days as one of the labels. The input data with conditional labels and load time series are decomposed into several sub-modes by the variational mode decomposition (VMD), which transforms the load forecasting into several sub-forecasting. Then, the CGAN generator is to capture the internal feature of each mode with the CNN and generate fake samples, while the CGAN discriminator is modified with a semi-supervised regression layer to extract the nonlinear and dynamic behaviors of the dataset and perform precise STLF. The final forecasting results are obtained by aggregating the results of all sub-mode. The generator and discriminator of the CGAN form a min–max game to improve the sample generation ability and reduce forecasting errors. The simulation results show that the STLF accuracy with the proposed model is significantly improved.