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A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model

Author

Listed:
  • Ling Liu

    (Power China Jiangxi Electric Power Engineering Co., Ltd., Nanchang 330096, China)

  • Fang Liu

    (School of Automation, Central South University, Changsha 410083, China)

  • Yuling Zheng

    (School of Automation, Central South University, Changsha 410083, China)

Abstract

Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based Takagi-Sugeno (T-S) fuzzy model. Firstly, the correlation analysis is used to filter redundant information. Furthermore, a T-S fuzzy model, which integrates fuzzy c-means (FCM) for the fuzzy division of input variables and DBN for fuzzy subsets forecasting, is developed. Finally, the proposed method is compared to a benchmark DBN method and the T-S fuzzy model in case studies. The numerical results show the feasibility and flexibility of the proposed ultra-short-term PV power forecasting approach.

Suggested Citation

  • Ling Liu & Fang Liu & Yuling Zheng, 2021. "A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model," Energies, MDPI, vol. 14(20), pages 1-10, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6447-:d:652359
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    References listed on IDEAS

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    Cited by:

    1. Jiaan Zhang & Yan Hao & Ruiqing Fan & Zhenzhen Wang, 2023. "An Ultra-Short-Term PV Power Forecasting Method for Changeable Weather Based on Clustering and Signal Decomposition," Energies, MDPI, vol. 16(7), pages 1-15, March.

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    Keywords

    DBN; T-S fuzzy; PV power; forecasting;
    All these keywords.

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