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Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting

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  • Liu, Yanli
  • Wang, Junyi

Abstract

With the increasing penetration of wind power, probabilistic forecasting becomes critical to quantifying wind power uncertainties and guiding power system operations. This paper proposes a transfer learning based probabilistic wind power forecasting method. Model-based transfer learning is utilized to construct the multi-layer extreme learning machine (MLELM). The output mapping factors of MLELM are further optimized through the particle swarm optimization (PSO) with the objective of minimizing the quantile evaluation indexes. Joint distribution adaptation (JDA) is utilized to update the weights of MLELM to accommodate variable wind power output. Test results on the practical wind farms in China shows that the proposed method can provide more accurate quantile forecasting results with better nonlinear fitting ability compared with other quantile forecasting methods.

Suggested Citation

  • Liu, Yanli & Wang, Junyi, 2022. "Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001866
    DOI: 10.1016/j.apenergy.2022.118729
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    1. Liu, Xin & Cao, Zheming & Zhang, Zijun, 2021. "Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning," Energy, Elsevier, vol. 217(C).
    2. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    3. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    4. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    5. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
    6. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    7. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    8. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    9. Wu, Zhuochun & Xia, Xiangjie & Xiao, Liye & Liu, Yilin, 2020. "Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 261(C).
    10. P. Pinson, 2012. "Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 555-576, August.
    11. Yan, Jie & Liu, Yongqian & Han, Shuang & Wang, Yimei & Feng, Shuanglei, 2015. "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1322-1330.
    12. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    13. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
    14. Yin, Hao & Ou, Zuhong & Fu, Jiajin & Cai, Yongfeng & Chen, Shun & Meng, Anbo, 2021. "A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture," Energy, Elsevier, vol. 234(C).
    15. Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
    16. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
    17. Sun, Mucun & Feng, Cong & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization," Applied Energy, Elsevier, vol. 238(C), pages 1497-1505.
    18. Moller, Jan Kloppenborg & Nielsen, Henrik Aalborg & Madsen, Henrik, 2008. "Time-adaptive quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1292-1303, January.
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