IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i17p6211-d1226153.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/17/6211/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/17/6211/
    Download Restriction: no
    ---><---

    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. 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).
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    2. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George J. Anders, 2024. "A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets," Energies, MDPI, vol. 17(10), pages 1-28, May.
    3. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    4. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
    5. Liu, Wenhui & Bai, Yulong & Yue, Xiaoxin & Wang, Rui & Song, Qi, 2024. "A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM," Energy, Elsevier, vol. 294(C).
    6. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
    7. Hu, Rongxing & Shirsat, Ashwin & Muthukaruppan, Valliappan & Li, Yiyan & Zhang, Si & Tang, Wenyuan & Baran, Mesut & Lu, Ning, 2024. "Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience," Applied Energy, Elsevier, vol. 356(C).
    8. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).
    9. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Jesus Lopez-Sotelo & David Celeita, 2023. "An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture," Energies, MDPI, vol. 16(19), pages 1-24, September.
    10. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    11. Saravanakumar Venkatesan & Yongyun Cho, 2024. "Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture," Energies, MDPI, vol. 17(17), pages 1-29, August.
    12. Xinyue Fu & Zhongkai Feng & Xinru Yao & Wenjie Liu, 2023. "A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction," Energies, MDPI, vol. 16(15), pages 1-23, July.
    13. Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    14. Sareen, Karan & Panigrahi, Bijaya Ketan & Shikhola, Tushar & Chawla, Astha, 2023. "A robust De-Noising Autoencoder imputation and VMD algorithm based deep learning technique for short-term wind speed prediction ensuring cyber resilience," Energy, Elsevier, vol. 283(C).
    15. Dong, Xiaochong & Sun, Yingyun & Dong, Lei & Li, Jian & Li, Yan & Di, Lei, 2023. "Transferable wind power probabilistic forecasting based on multi-domain adversarial networks," Energy, Elsevier, vol. 285(C).
    16. Seyed Mohammad Sharifhosseini & Taher Niknam & Mohammad Hossein Taabodi & Habib Asadi Aghajari & Ehsan Sheybani & Giti Javidi & Motahareh Pourbehzadi, 2024. "Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications," Energies, MDPI, vol. 17(21), pages 1-35, October.
    17. Te Li & Mengze Zhang & Yan Zhou, 2024. "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers 2410.15286, arXiv.org.
    18. Han, Shuo & Yuan, Yifan & He, Mengjiao & Zhao, Ziwen & Xu, Beibei & Chen, Diyi & Jurasz, Jakub, 2024. "A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system," Applied Energy, Elsevier, vol. 356(C).
    19. Gao, Fang & Xu, Zidong & Yin, Linfei, 2024. "Bayesian deep neural networks for spatio-temporal probabilistic optimal power flow with multi-source renewable energy," Applied Energy, Elsevier, vol. 353(PA).
    20. Xiaomei Ma & Yongqian Liu & Jie Yan & Han Wang, 2023. "A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study," Energies, MDPI, vol. 16(7), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6211-:d:1226153. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.