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

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.

Keywords

Electricity consumption / grey system theory / prediction model / fractional order

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