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Short-term load forecasting of industrial customers based on SVMD and XGBoost

Wang, Yuanyuan and Sun, Shanfeng and Chen, Xiaoqiao and Zeng, Xiangjun and Kong, Yang and Chen, Jun and Guo, Yongsheng and Wang, Tingyuan (2021) Short-term load forecasting of industrial customers based on SVMD and XGBoost. International Journal of Electrical Power and Energy Systems, 129 . Art. No. 106830. ISSN 0142-0615. doi:10.1016/j.ijepes.2021.106830. https://resolver.caltech.edu/CaltechAUTHORS:20210506-101710133

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Abstract

The electricity consumption by industrial customers in the society accounts for a significant proportion of the total electrical energy. Thus, it is of great significance for demand-side electrical energy management to develop an accurate method for short-term load forecasting for industrial customers. Unlike traditional load forecasting on system-level, the load forecasting of individual industrial customer is more challenging due to its significant volatility and uncertainty. We propose an adaptive decomposition method based on VMD and SampEn (SVMD) to decompose the raw load data into a trend series and a set of fluctuation sub-series, and then establish the corresponding prediction model (line regression model for the trend series and XGBoost regression model for each fluctuation sub-series). The hyper-parameters of XGBoost are optimized by bayesian optimization algorithm (BOA). Furthermore, relevant factors that affect the electricity consumption behavior of industrial customers are considered in order to further improve the accuracy of the hybrid method. The proposed method is tested in multiple scenarios with different industrial customers of China and Irish. The results show that the proposed model has significantly improved performance over the contrast models in state-of-the-art load forecasting.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.ijepes.2021.106830DOIArticle
ORCID:
AuthorORCID
Wang, Yuanyuan0000-0002-9575-3712
Sun, Shanfeng0000-0002-9120-357X
Chen, Xiaoqiao0000-0003-4685-3466
Kong, Yang0000-0002-3817-9785
Guo, Yongsheng0000-0003-4235-9807
Additional Information:© 2021 Elsevier Ltd. Received 3 September 2020, Revised 5 December 2020, Accepted 18 January 2021, Available online 26 February 2021. This work is supported by the National Natural Science Foundation of China (No. 51777014). Hunan Provincial Key Research and Development Program (No. 2018GK2057). Research projects funded by Department of Education of Hunan Province of China (18A124). Changsha Science and Technology Project (kq1901104). Hunan Graduate Research and Innovation Project (CX20190686). CRediT authorship contribution statement: Yuanyuan Wang: Conceptualization, Methodology, Writing - review & editing, Supervision. Shanfeng Sun: Writing - original draft. Xiaoqiao Chen: Software, Visualization, Investigation. Xiangjun Zeng: Supervision. Yang Kong: Data curation. Jun Chen: Writing - original draft. Yongsheng Guo: Writing - review & editing. Tingyuan Wang: Writing - review & editing. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China51777014
Hunan Provincial Key Research and Development Program2018GK2057
Hunan Province of China18A124
Changsha Science and Technology Projectkq1901104
Hunan Graduate Research and Innovation ProjectCX20190686
Subject Keywords:Load forecasting; Industrial customers; Adaptive VMD; XGBoost; BOA; Relevant factors
DOI:10.1016/j.ijepes.2021.106830
Record Number:CaltechAUTHORS:20210506-101710133
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210506-101710133
Official Citation:Yuanyuan Wang, Shanfeng Sun, Xiaoqiao Chen, Xiangjun Zeng, Yang Kong, Jun Chen, Yongsheng Guo, Tingyuan Wang, Short-term load forecasting of industrial customers based on SVMD and XGBoost, International Journal of Electrical Power & Energy Systems, Volume 129, 2021, 106830, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2021.106830. (https://www.sciencedirect.com/science/article/pii/S0142061521000703)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108988
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 May 2021 17:26
Last Modified:06 May 2021 17:26

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