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

Huimin Chen, Xiaoyan Sun, Liqin Fu and Bokui Chen
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Huimin Chen: School of Artificial Intelligence, Beijing Institute of Economics and Management, Beijing 100102, P. R. China
Xiaoyan Sun: ��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
Liqin Fu: School of Artificial Intelligence, Beijing Institute of Economics and Management, Beijing 100102, P. R. China
Bokui Chen: �Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 10, 1-14

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: Grey model; weighted Markov model; industrial electricity consumption prediction; prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501304

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