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EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Author

Listed:
  • Peng, Xiaosheng
  • Wang, Hongyu
  • Lang, Jianxun
  • Li, Wenze
  • Xu, Qiyou
  • Zhang, Zuowei
  • Cai, Tao
  • Duan, Shanxu
  • Liu, Fangjie
  • Li, Chaoshun

Abstract

Effective wind-power prediction enhances the adaptability of a wind power system to the instability of wind power, which is beneficial for load and frequency regulation, helping to convert wind power to electricity and connect wind power to the grid safely. Moreover, the use of numerical weather prediction (NWP) to predict the probability results of wind power is a matter of general concern in the field of wind power prediction, and deep neural networks have become an indispensable research tool. In this study, a new neural-network prediction model called EALSTM-QR was developed for wind-power prediction considering the input of NWP and the deep-learning method. In the model, there are four main levels: Encoder, Attention, bidirectional long short-term memory (LSTM), and quantile regression (QR). The combination inputs contain historical wind-power data and the features extracted and obtained from the NWP through the Encoder and Attention levels. The bidirectional LSTM is used to generate wind-power time-series probability prediction results. The QR method and confidence interval limits are used to obtain the final prediction intervals. The proposed method was compared with several interval prediction models and probability prediction models based on neural networks for wind-power prediction by using datasets from wind farms in China. The results indicated that the proposed EALSTM-QR has good accuracy and reliability for the prediction of intervals and probabilities.

Suggested Citation

  • Peng, Xiaosheng & Wang, Hongyu & Lang, Jianxun & Li, Wenze & Xu, Qiyou & Zhang, Zuowei & Cai, Tao & Duan, Shanxu & Liu, Fangjie & Li, Chaoshun, 2021. "EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning," Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s0360544220327997
    DOI: 10.1016/j.energy.2020.119692
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    14. Liu, Hongyi & Han, Hua & Sun, Yao & Shi, Guangze & Su, Mei & Liu, Zhangjie & Wang, Hongfei & Deng, Xiaofei, 2022. "Short-term wind power interval prediction method using VMD-RFG and Att-GRU," Energy, Elsevier, vol. 251(C).
    15. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    16. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    17. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(C).
    18. Shijun Wang & Chun Liu & Kui Liang & Ziyun Cheng & Xue Kong & Shuang Gao, 2022. "Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    19. Li, Ruilian & Zeng, Deliang & Li, Tingting & Ti, Baozhong & Hu, Yong, 2023. "Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer," Energy, Elsevier, vol. 269(C).
    20. Meng, Anbo & Chen, Shun & Ou, Zuhong & Ding, Weifeng & Zhou, Huaming & Fan, Jingmin & Yin, Hao, 2022. "A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization," Energy, Elsevier, vol. 238(PB).
    21. Li, Jingrui & Wang, Jianzhou & Zhang, Haipeng & Li, Zhiwu, 2022. "An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China," Renewable Energy, Elsevier, vol. 201(P1), pages 766-779.
    22. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
    23. Banteng Liu & Yangqing Xie & Ke Wang & Lizhe Yu & Ying Zhou & Xiaowen Lv, 2023. "Short-Term Multi-Step Wind Direction Prediction Based on OVMD Quadratic Decomposition and LSTM," Sustainability, MDPI, vol. 15(15), pages 1-18, July.
    24. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2023. "Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework," Energy, Elsevier, vol. 278(PA).

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