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A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

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  • Kaitong Wu

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Xiangang Peng

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Zilu Li

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Wenbo Cui

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Haoliang Yuan

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Chun Sing Lai

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Loi Lei Lai

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.

Suggested Citation

  • Kaitong Wu & Xiangang Peng & Zilu Li & Wenbo Cui & Haoliang Yuan & Chun Sing Lai & Loi Lei Lai, 2022. "A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection," Energies, MDPI, vol. 15(15), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5410-:d:872613
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    References listed on IDEAS

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    1. Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
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    3. Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei & Xu, Zhao & McCulloch, Malcolm D. & Wong, Kit Po, 2017. "A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 439-451.
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    5. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
    6. Xiaomei Wu & Chun Sing Lai & Chenchen Bai & Loi Lei Lai & Qi Zhang & Bo Liu, 2020. "Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction," Energies, MDPI, vol. 13(14), pages 1-21, July.
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    Cited by:

    1. Liu, Wencheng & Mao, Zhizhong, 2024. "Short-term photovoltaic power forecasting with feature extraction and attention mechanisms," Renewable Energy, Elsevier, vol. 226(C).
    2. Xin Ren & Yimei Wang & Zhi Cao & Fuhao Chen & Yujia Li & Jie Yan, 2023. "Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting," Energies, MDPI, vol. 16(17), pages 1-13, August.

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