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Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network

Author

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  • Cai Tao

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Junjie Lu

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jianxun Lang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xiaosheng Peng

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Kai Cheng

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shanxu Duan

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In this paper, a hybrid model that considers both accuracy and efficiency is proposed to predict photovoltaic (PV) power generation. To achieve this, improved forward feature selection is applied to obtain the optimal feature set, which aims to remove redundant information and obtain related features, resulting in a significant improvement in forecasting accuracy and efficiency. The prediction error is irregularly distributed. Thus, a bias compensation–long short-term memory (BC–LSTM) network is proposed to minimize the prediction error. The experimental results show that the new feature selection method can improve the prediction accuracy by 0.6% and the calculation efficiency by 20% compared to using feature importance identification based on LightGBM. The BC–LSTM network can improve accuracy by 0.3% using about twice the time compared with the LSTM network, and the hybrid model can further improve prediction accuracy and efficiency based on the BC–LSTM network.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3086-:d:562424
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    References listed on IDEAS

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    Cited by:

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    3. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    4. Edna S. Solano & Payman Dehghanian & Carolina M. Affonso, 2022. "Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection," Energies, MDPI, vol. 15(19), pages 1-18, September.
    5. Hiroki Yamamoto & Junji Kondoh & Daisuke Kodaira, 2022. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation," Energies, MDPI, vol. 15(15), pages 1-17, July.
    6. 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.
    7. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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