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An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

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  • Honglu Zhu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
    School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China
    Beijing Key Laboratory of New and Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Weiwei Lian

    (School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Lingxing Lu

    (School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Songyuan Dai

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
    School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China
    Beijing Key Laboratory of New and Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Yang Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

Suggested Citation

  • Honglu Zhu & Weiwei Lian & Lingxing Lu & Songyuan Dai & Yang Hu, 2017. "An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window," Energies, MDPI, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1542-:d:114208
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    References listed on IDEAS

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    Cited by:

    1. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
    4. Lin Yang & Jikai Bi & Yanpeng Hao & Lupeng Nian & Zijun Zhou & Licheng Li & Yifan Liao & Fuzeng Zhang, 2018. "A Recognition Method of the Hydrophobicity Class of Composite Insulators Based on Features Optimization and Experimental Verification," Energies, MDPI, vol. 11(4), pages 1-13, March.
    5. Yu, Cao & Wang, Haizheng & Yao, Jianxi & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2020. "A dynamic alarm threshold setting method for photovoltaic array and its application," Renewable Energy, Elsevier, vol. 158(C), pages 13-22.
    6. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
    7. Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.

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