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Advancing Grey Modeling with a Novel Time-Varying Approach for Predicting Solar Energy Generation in the United States

Author

Listed:
  • Ke Zhou

    (Faculty of Management Engineering, Anhui Institute of Information Technology, Wuhu 241000, China)

  • Ziji Zhao

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Lin Xia

    (Faculty of Big Data Artificial Intelligence, Anhui Institute of Information Technology, Wuhu 241000, China)

  • Jinghua Wu

    (Faculty of Big Data Artificial Intelligence, Anhui Institute of Information Technology, Wuhu 241000, China)

Abstract

This paper proposes a novel time-varying discrete grey model (TVDGM(1,1)) to precisely forecast solar energy generation in the United States. First, the model utilizes the anti-forgetting curve as the weight function for the accumulation of the original sequence, which effectively ensures the prioritization of new information within the model. Second, the time response function of the model is derived through mathematical induction, which effectively addresses the common jump errors encountered when transitioning from difference equations to differential equations in traditional grey models. Research shows that compared to seven other methods, this model achieves better predictive performance, with an error rate of only 2.95%. Finally, this method is applied to forecast future solar energy generation in the United States, and the results indicate an average annual growth rate of 23.67% from 2024 to 2030. This study advances grey modeling techniques using a novel time-varying approach while providing critical technical and data support for energy planning.

Suggested Citation

  • Ke Zhou & Ziji Zhao & Lin Xia & Jinghua Wu, 2024. "Advancing Grey Modeling with a Novel Time-Varying Approach for Predicting Solar Energy Generation in the United States," Sustainability, MDPI, vol. 16(24), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11112-:d:1546833
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    References listed on IDEAS

    as
    1. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    2. Wang, Yong & Sun, Lang & Yang, Rui & He, Wenao & Tang, Yanbing & Zhang, Zejia & Wang, Yunhui & Sapnken, Flavian Emmanuel, 2023. "A novel structure adaptive fractional derivative grey model and its application in energy consumption prediction," Energy, Elsevier, vol. 282(C).
    3. Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
    4. Xia, Lin & Ren, Youyang & Wang, Yuhong & Pan, Yangyang & Fu, Yiyang, 2024. "Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model," Renewable Energy, Elsevier, vol. 233(C).
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