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PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Author

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  • Wentao Ma

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
    Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory, Guilin University of Electronic Technology, Guilin 541004, China)

  • Lihong Qiu

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Fengyuan Sun

    (Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory, Guilin University of Electronic Technology, Guilin 541004, China)

  • Sherif S. M. Ghoneim

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Jiandong Duan

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

Suggested Citation

  • Wentao Ma & Lihong Qiu & Fengyuan Sun & Sherif S. M. Ghoneim & Jiandong Duan, 2022. "PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type," Energies, MDPI, vol. 15(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5231-:d:866386
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    References listed on IDEAS

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    1. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    2. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Fu, Wenlong & Fang, Ping & Wang, Kai & Li, Zhenxing & Xiong, Dongzhen & Zhang, Kai, 2021. "Multi-step ahead short-term wind speed forecasting approach coupling variational mode decomposition, improved beetle antennae search algorithm-based synchronous optimization and Volterra series model," Renewable Energy, Elsevier, vol. 179(C), pages 1122-1139.
    4. Chen, Xiang & Ding, Kun & Zhang, Jingwei & Han, Wei & Liu, Yongjie & Yang, Zenan & Weng, Shuai, 2022. "Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM," Energy, Elsevier, vol. 248(C).
    5. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    6. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
    7. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    8. Yang Hu & Weiwei Lian & Yutong Han & Songyuan Dai & Honglu Zhu, 2018. "A Seasonal Model Using Optimized Multi-Layer Neural Networks to Forecast Power Output of PV Plants," Energies, MDPI, vol. 11(2), pages 1-17, February.
    9. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
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    Cited by:

    1. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.

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