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Dual-meta pool method for wind farm power forecasting with small sample data

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  • Liu, Ling
  • Wang, Jujie
  • Li, Jianping
  • Wei, Lu

Abstract

The power prediction is important for the safe and efficient operation of new wind farms with small data. However, there is a lack of research on improving the prediction accuracy. Here we propose a novel dual-meta pool model to realize multi-step prediction based on learning the data knowledge contained in relevant wind farms. By analysing the essence of neural network, we defined meta-data to express and extract the data knowledge. For meta-data classification, we designed a new unsupervised classification method based on the accuracy of data tail part. A convolutional neural network is used to learn the mapping relationship between meta-data and their labels. To make multi-step prediction, we proposed a novel meta-method which contains Hilbert spatial transformation, data extension and neural network. To verify the performance, eight comparison models are used to predict the last 30 data of two wind farms which only contain 96 data. The results show that the proposed dual-meta pool method has smaller prediction errors than other models, which average mean absolute errors are 13.33 and 2.42, respectively.

Suggested Citation

  • Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Dual-meta pool method for wind farm power forecasting with small sample data," Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222033904
    DOI: 10.1016/j.energy.2022.126504
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    1. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
    2. Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
    3. Pan, Xiaoxin & Wang, Long & Wang, Zhongju & Huang, Chao, 2022. "Short-term wind speed forecasting based on spatial-temporal graph transformer networks," Energy, Elsevier, vol. 253(C).
    4. Wang, Fei & Tong, Shuang & Sun, Yiqian & Xie, Yongsheng & Zhen, Zhao & Li, Guoqing & Cao, Chunmei & Duić, Neven & Liu, Dagui, 2022. "Wind process pattern forecasting based ultra-short-term wind speed hybrid prediction," Energy, Elsevier, vol. 255(C).
    5. Kou, Peng & Gao, Feng & Guan, Xiaohong, 2015. "Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts," Renewable Energy, Elsevier, vol. 80(C), pages 286-300.
    6. Rogoza, Walery, 2019. "Method for the prediction of time series using small sets of experimental samples," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 108-122.
    7. Khodayar, Mahdi & Saffari, Mohsen & Williams, Michael & Jalali, Seyed Mohammad Jafar, 2022. "Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting," Energy, Elsevier, vol. 254(PB).
    8. 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).
    9. Yin, Shi & Liu, Hui, 2022. "Wind power prediction based on outlier correction, ensemble reinforcement learning, and residual correction," Energy, Elsevier, vol. 250(C).
    10. Liu, Ling & Wang, Jujie, 2021. "Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network," Applied Energy, Elsevier, vol. 292(C).
    11. Higgins, P. & Foley, A.M. & Douglas, R. & Li, K., 2014. "Impact of offshore wind power forecast error in a carbon constraint electricity market," Energy, Elsevier, vol. 76(C), pages 187-197.
    12. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    13. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    14. Niu, Dongxiao & Sun, Lijie & Yu, Min & Wang, Keke, 2022. "Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model," Energy, Elsevier, vol. 254(PA).
    15. Jin, Haiyan & Cui, Ningmin & Cai, Lei & Meng, Jinhao & Li, Junxin & Peng, Jichang & Zhao, Xinchao, 2023. "State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    16. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    17. Wang, Zhiwen & Shen, Chen & Liu, Feng, 2018. "A conditional model of wind power forecast errors and its application in scenario generation," Applied Energy, Elsevier, vol. 212(C), pages 771-785.
    18. Wang, Xuguang & Ren, Huan & Zhai, Junhai & Xing, Hongjie & Su, Jie, 2022. "Adaptive support segment based short-term wind speed forecasting," Energy, Elsevier, vol. 249(C).
    19. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
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    Cited by:

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    3. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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