Short-term power load forecasting based on combined kernel Gaussian process hybrid model

¹ China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663
² China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663
³ China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663
⁴ China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663
⁵ China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663
⁶ China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663
⁷ China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou 510663

^* Corresponding author’s e-mail: liangly1@csg.cn

Abstract

As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals. However, the traditional GPM often uses a single kernel function, and the prediction effect is not optimal. Therefore, this paper will combine a variety of existing kernel to build a new kernel, and use it for load sequence prediction. In the electricity load prediction experiments, the prediction characteristics of the load sequences are first analyzed, and then the prediction is made based on the optimal hybrid kernel function constructed by GPM and compared with the traditional prediction model. The results show that the GPM based on the hybrid kernel is not only superior to the single kernel GPM but also superior to some traditional prediction models such as ridge regression, kernel regression and GP.