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Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Wang, Yuanyuan and Kong, Yang and Tang, Xiafei and Chen, Xiaoqiao and Xu, Yao and Chen, Jun and Sun, Shanfeng and Guo, Yongsheng and Chen, Yuhao (2020) Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model. IEEE Access, 8 . pp. 160858-160870. ISSN 2169-3536. https://resolver.caltech.edu/CaltechAUTHORS:20200911-084739596

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/access.2020.3020799DOIArticle
ORCID:
AuthorORCID
Wang, Yuanyuan0000-0002-9575-3712
Kong, Yang0000-0002-3817-9785
Chen, Jun0000-0001-7095-5142
Sun, Shanfeng0000-0002-9120-357X
Guo, Yongsheng0000-0003-4235-9807
Additional Information:© 2020 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This work was supported in part by the National Natural Science Foundation of China under Grant 51777014, in part by the Science and Technology Projects of Hunan Province under Grant 2018GK2057, in part by the Department of Education of Hunan Province of China under Grant 18A124, in part by the Changsha Science and Technology Project under Grant kq1901104, and in part by the Hunan Graduate Research and Innovation Project under Grant CX20190686. The associate editor coordinating the review of this manuscript and approving it for publication was Sotirios Goudos.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China51777014
Science and Technology Projects of Hunan Province2018GK2057
Department of Education of Hunan Province of China18A124
Changsha Science and Technology Projectkq1901104
Hunan Graduate Research and Innovation ProjectCX20190686
Subject Keywords:Short-term load forecasting, industrial customers, hidden Markov model, ensemble learning
Record Number:CaltechAUTHORS:20200911-084739596
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200911-084739596
Official Citation:Y. Wang et al., "Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model," in IEEE Access, vol. 8, pp. 160858-160870, 2020, doi: 10.1109/ACCESS.2020.3020799
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105318
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:11 Sep 2020 16:00
Last Modified:11 Sep 2020 16:00

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