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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 (2020) Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM. IEEE Transactions on Power Systems . ISSN 0885-8950. (In Press) 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 Convolution 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
Kong, Yang0000-0002-3817-9785
Sun, Shanfeng0000-0002-9120-357X
Guo, Yongsheng0000-0003-4235-9807
Additional Information:© 2020 IEEE. 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 under Grant (18A124). Changsha Science and Technology Project (kq1901104). Hunan Graduate Research and Innovation Project (CX20190686). Changsha University of Science and Technology Research and Innovation Project (CX2020SS52).
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
Record Number:CaltechAUTHORS:20201008-083808624
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201008-083808624
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:08 Oct 2020 17:22

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