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Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China

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  • Du, Xiaoyi
  • Wu, Dongdong
  • Yan, Yabo

Abstract

In this paper, GM(1,Nr) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm; (2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables; and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,Nr) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions.

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  • Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023210
    DOI: 10.1016/j.energy.2022.125439
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