Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model
Yuanyuan Wang, Yang Kong, Xiafei Tang, Xiaoqiao Chen, Xu Yao, Jun Chen, Shanfeng Sun, Yongsheng Guo, Yuhao Chen
Abstract
Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy.