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A novel hybrid grey system forecasting model based on seasonal fluctuation characteristics for electricity consumption in primary industry

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  • Li, Cheng
  • Qi, Qi

Abstract

As a critical economic index, the annual electricity consumption plays an essential role in reflecting the domestic economic environment. For the sustainable development of the electricity power industry, an accurate electricity consumption forecasting model can not only guide the power generation work in advance but also help to determine the regulation law of electricity economic development. To accurately describe the seasonal fluctuation characteristics of electricity consumption in China's primary industry, Fourier modified grey forecasting model based on seasonal fluctuation characteristics and initial condition optimization (FDSGM(1,1,x(β),γ)) was put forward. Based on the empirical data of seasonal electricity consumption in China's primary industry, comparing with SGM(1,1), RSGM(1,1) and DSGM(1,1), the minimum mean absolute percentage errors (MAPEs) in the training set was reduced to 0.2 %, while that in the testing set was also reduced to 6.02 %, which was significantly better than those of the previous three forecasting models and was also markedly better than the prediction accuracy of reference [15]. The results indicated that FDSGM(1,1,x(β),γ) could identify seasonal fluctuations and variations and was a reliable seasonal forecasting tool that could be used to ensure sustainable development of electricity markets.

Suggested Citation

  • Li, Cheng & Qi, Qi, 2024. "A novel hybrid grey system forecasting model based on seasonal fluctuation characteristics for electricity consumption in primary industry," Energy, Elsevier, vol. 287(C).
    • Handle: RePEc:eee:energy:v:287:y:2024:i:c:s0360544223029791
    DOI: 10.1016/j.energy.2023.129585
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    References listed on IDEAS

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    1. Li, Xuetao & Wang, Ziwei & Yang, Chengying & Bozkurt, Ayhan, 2024. "An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms," Energy, Elsevier, vol. 296(C).

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