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Research PaperNo Access

Prediction of industrial electricity consumption based on grey cluster weighted Markov model

https://orcid.org/0009-0005-8032-6020

School of Artificial Intelligence, Beijing Institute of Economics and Management, Beijing 100102, P. R. China

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,

https://orcid.org/0000-0002-4869-8001

School of Management Science and Engineering, Guangxi University of Finance and Economics, Nanning, Guangxi 530007, P. R. China

College of Traffic and Transportation, Nanning University, Nanning, Guangxi 530200, P. R. China

E-mail Address: sxy11@mail.ustc.edu.cn

Corresponding author.

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,

https://orcid.org/0000-0001-8526-2445

School of Artificial Intelligence, Beijing Institute of Economics and Management, Beijing 100102, P. R. China

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, and

https://orcid.org/0000-0002-4947-5619

Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, P. R. China

E-mail Address: chenbk@tsinghua.edu.cn

Corresponding author.

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https://doi.org/10.1142/S0129183124501304Cited by:0 (Source: Crossref)

Abstract

Accurate prediction of industrial electricity consumption is not only beneficial to maintaining the steady development of the economy but also to conserving energy. To improve the prediction accuracy of industrial electricity consumption, a grey cluster weighted Markov model is proposed. It is applied to predict the industrial electricity consumption in four different regions in China. The prediction results are compared with the traditional discrete grey prediction model, which shows that the present model is more effective in these aspects of prediction accuracy, stability and extensibility. The research can provide theoretical references for the “West-East electricity transmission project” in China.

Keywords:

PACS: 02.70.−c, 88.05.Vx, 89.20.−a

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Vol. 35, No. 10

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Downloaded 33 times

History

Received 13 December 2023

Accepted 27 February 2024

Published: 8 April 2024

Keywords