An effective dimensionality reduction approach for short-term load forecasting

Journal article

Yang, Yang, Wang, Zijin, Gao, Yuchao, Wu, Jinran, Zhao, Shangrui and Ding, Zhe. (2022). An effective dimensionality reduction approach for short-term load forecasting. Electric Power Systems Research. 210, p. Article 108150. https://doi.org/10.1016/j.epsr.2022.108150
AuthorsYang, Yang, Wang, Zijin, Gao, Yuchao, Wu, Jinran, Zhao, Shangrui and Ding, Zhe
Abstract

Accurate power load forecasting has a significant effect on a smart grid by ensuring effective supply and dispatching of power. However, electric load data generally possesses the characteristics of nonlinearity, periodicity, and seasonality. For complex electric load systems, the presence of redundant information potentially reduces the real pattern extraction for load forecasting. Bearing in mind these issues, we propose an effective forecasting model in which a feature extraction module is introduced that is combined with the variational mode decomposition (VMD) with the variational autoencoder (VAE). In this combination, VMD is utilized for decomposing complex load series and VAE is used to filter the redundant information (noises) from each decomposed series. With two real data sets from China, we demonstrate that the proposed model can achieve highly accurate predictions, as we find the mean absolute percentage error (MAPE) values for one-step-ahead prediction to be 1% (Nanjing) and 0.8% (Taixing), respectively.

Keywordsdeep learning; decomposition-ensemble method; feature extraction; load forecasting
Year2022
JournalElectric Power Systems Research
Journal citation210, p. Article 108150
PublisherElsevier B.V.
ISSN0378-7796
Digital Object Identifier (DOI)https://doi.org/10.1016/j.epsr.2022.108150
Scopus EID2-s2.0-85131720959
Open accessPublished as green open access
Page range1-12
FunderAustralian Research Council (ARC)
Chinese Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China (NSFC)
Natural Science Foundation of Jiangsu Province
Nanjing University of Posts and Telecommunications (NUPTSF), China
Author's accepted manuscript
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Output statusPublished
Publication dates
Online11 Jun 2022
Publication process dates
Accepted29 May 2022
Deposited07 Jul 2023
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
CE140100049
WUT: 213114009
61873130
61833011
BK20191377
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