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A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

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Abstract

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure. However, the regional power system is complicated and uncertain, affected by multiple factors including climate, population and economy. This paper incorporates structure expansion, parameter optimization and rolling mechanism into a system forecasting framework, and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption, improving the accuracy of the traditional grey models. The optimal fractional order is obtained by using the particle swarm optimization algorithm, which enhances the model adaptability. Then, the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province. Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026. Compared with other seven benchmark prediction models, the proposed grey system model performs best in terms of both simulation and prediction performance metrics, providing scientific reference for regional energy planning and electricity market operation.

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Data Availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to extend their sincere gratitude to the referees for their valuable feedback and suggestions, which significantly contributed to the improvement of the quality of this paper. This work was supported in part by the National Social Science Fund of China under Grant No. 22FGLB035, and Fujian Provincial Federation of Social Sciences under Grant No. FJ2023B109.

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Authors and Affiliations

  1. College of Business Administration, Huaqiao University, Quanzhou, 362021, China

    Wenhao Zhou & Hailin Li

  2. College of Business Administration, Capital University of Economics and Business, Beijing, 100070, China

    Zhiwei Zhang

Authors
  1. Wenhao Zhou
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  2. Hailin Li
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  3. Zhiwei Zhang
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Correspondence to Hailin Li.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Wenhao Zhou is a Ph.D. candidate at the College of Business Administration in Huaqiao University. He received his master’s degree in management from the School of Management Science and Engineering, Chongqing Technology and Business University. His main research interests include system forecasting and simulation, data science, and innovation management.

Hailin Li is a professor of College of Business Administration in Huaqiao University. He obtained his Ph.D. degree in management science and engineering from Dalian University of Technology. His research interests include data mining, time series and machine learning.

Zhiwei Zhang is a Ph.D. candidate at the College of Business Administration in Capital University of Economics and Business. She received her Master’s degree in international business from Business School, Chongqing Technology and Business University. Her research interests include data analysis and grey theory.

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Zhou, W., Li, H. & Zhang, Z. A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption. J. Syst. Sci. Syst. Eng. 33, 207–231 (2024). https://doi.org/10.1007/s11518-024-5590-3

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  • DOI: https://doi.org/10.1007/s11518-024-5590-3

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