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Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting

Author

Listed:
  • Xin Ren

    (China Huaneng Clean Energy Research Institute, Beijing 102209, China)

  • Yimei Wang

    (China Huaneng Clean Energy Research Institute, Beijing 102209, China)

  • Zhi Cao

    (China Huaneng Group Co., Ltd., Beijing 100031, China)

  • Fuhao Chen

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yujia Li

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Jie Yan

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

Abstract

A common dilemma with deep-learning-based solar power forecasting models is their heavy dependence on a large amount of training data. Few-Shot Solar Power Forecasting (FSSPF) has been investigated in this paper, which aims to obtain accurate forecasting models with limited training data. Integrating Transfer Learning and Meta-Learning, approaches of Feature Transfer and Rapid Adaptation (FTRA), have been proposed for FSSPF. Specifically, the adopted model will be divided into Transferable learner and Adaptive learner. Using massive training data from source solar plants, Transferable learner and Adaptive learner will be pre-trained through a Transfer Learning and Meta-Learning algorithm, respectively. Ultimately, the parameters of the Adaptive learner will undergo fine-tuning using the limited training data obtained directly from the target solar plant. Three open solar power forecasting datasets (GEFCom2014) were utilized to conduct 24-h-ahead FSSPF experiments. The results illustrate that the proposed FTRA is able to outperform other FSSPF approaches, under various amounts of training data as well as different deep-learning models. Notably, with only 10-day training data, the proposed FTRA can achieve an RMSR of 8.42%, which will be lower than the 0.5% achieved by the state-of-the-art approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6211-:d:1226153
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    References listed on IDEAS

    as
    1. Elham M. Al-Ali & Yassine Hajji & Yahia Said & Manel Hleili & Amal M. Alanzi & Ali H. Laatar & Mohamed Atri, 2023. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    2. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
    3. Manel Marweni & Mansour Hajji & Majdi Mansouri & Mohamed Fouazi Mimouni, 2023. "Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques," Energies, MDPI, vol. 16(12), pages 1-16, June.
    4. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    5. Sareen, Karan & Panigrahi, Bijaya Ketan & Shikhola, Tushar & Sharma, Rajneesh, 2023. "An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction," Energy, Elsevier, vol. 278(C).
    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. Hu, Jinxing & Li, Hongru, 2022. "A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm," Renewable Energy, Elsevier, vol. 185(C), pages 1139-1151.
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