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Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Wang, Yuanyuan and Chen, Jun and Chen, Xiaoqiao and Zeng, Xiangjun and Kong, Yang and Sun, Shanfeng and Guo, Yongsheng and Liu, Ying (2021) Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM. IEEE Transactions on Power Systems, 36 (3). pp. 1984-1997. ISSN 0885-8950. doi:10.1109/TPWRS.2020.3028133. https://resolver.caltech.edu/CaltechAUTHORS:20201008-083808624

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Abstract

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tpwrs.2020.3028133DOIArticle
ORCID:
AuthorORCID
Wang, Yuanyuan0000-0002-9575-3712
Chen, Jun0000-0001-7095-5142
Chen, Xiaoqiao0000-0003-4685-3466
Kong, Yang0000-0002-3817-9785
Sun, Shanfeng0000-0002-9120-357X
Guo, Yongsheng0000-0003-4235-9807
Additional Information:© 2020 IEEE. Manuscript received March 4, 2020; revised June 25, 2020 and August 30, 2020; accepted September 27, 2020. Date of publication October 1, 2020; date of current version April 19, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 51777014, in part by Hunan Provincial Key Research and Development Program under Grant 2018GK2057, in part by Research projects funded by Department of Education of Hunan Province of China under Grant 18A124, in part by Changsha Science and Technology Project kq1901104, in part by Hunan Graduate Research and Innovation Project under Grant CX20190686, and in part by Changsha University of Science and Technology Research and Innovation Project CX2020SS52. Paper no. TPWRS-00348-2020.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China51777014
Hunan Provincial Key Research and Development Program2018GK2057
Hunan Province Department of Education18A124
Changsha Science and Technology Projectkq1901104
Hunan Graduate Research and Innovation ProjectCX20190686
Changsha University of Science and TechnologyCX2020SS52
Subject Keywords:Short-term load forecasting, industrial customers, temporal convolutional network, light gradient boosting machine
Issue or Number:3
DOI:10.1109/TPWRS.2020.3028133
Record Number:CaltechAUTHORS:20201008-083808624
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201008-083808624
Official Citation:Y. Wang et al., "Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM," in IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 1984-1997, May 2021, doi: 10.1109/TPWRS.2020.3028133
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105910
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:08 Oct 2020 17:22
Last Modified:23 Apr 2021 17:41

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