Short-Term Load Forecasting Based on Integration of SVR and Stacking
Zhenqi Tan, Jing Zhang, Yu He, Ying Zhang, Guojiang Xiong, Ying Liu
Abstract
Selection of the kernel function by the support vector regression (SVR), for the purposes of load forecasting, is affected by the power load characteristics. The non-ideal SVR with a kernel function has low forecasting accuracy and poor generalization ability. A novel load forecasting method combining SVR and stacking is proposed in this paper. Base models are constructed based on SVRs with different kernel functions, then multiple base models are merged to obtain a base model layer via stacking algorithm. Finally, an SVR is connected as the meta-model layer. The stacking fusion model is composed of base model layer and meta-model layer. This model is trained with k-fold cross validation to enhance its generalization ability. An improved artificial fish swarm algorithm is employed to optimize the parameters to improve the forecasting accuracy of the stacking fusion model; speed variables are introduced to replace step lengths and improve the convergence speed and search ability. The forecasting accuracy and generalization ability of the proposed method are verified by comparative analysis.