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Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting

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  • Zhang, Yi-Ming
  • Wang, Hao

Abstract

Wind energy is one of the most widely used and fastest-growing renewable energy. Wind speed prediction is an efficient way to rationally dispatch wind power generation and ensure the stability of the power system. Typical deep learning algorithms, such as the convolution neural network (CNN), long short-term memory (LSTM), and their hybrid model (CNN-LSTM), have been extensively used for time-series prediction in various fields. The CNN-LSTM model exhibits superior forecasting performance by integrating the advantages of CNN and LSTM, but it fails to quantify the forecasting uncertainty that is critical for optimal management. This study proposes a probabilistic model to forecast day-ahead wind speed based on the CNN-bidirectional LSTM (BiLSTM) and deep ensemble strategy. Unlike purely statistical methods, the hybrid physical-statistical model that combines the numerical weather prediction (NWP) model and onsite measurements is employed to improve long-term forecasting accuracy. Specifically, the probabilistic CNN-BiLSTM model is developed by adjusting the network structure, and the uncertainty is optimized through a proper scoring rule. A combination of ensembles is then used to improve the robustness of probabilistic prediction. The spatial and temporal correlations of NWP data are both considered. The new probabilistic model is applied to forecast wind speed in measurements collected from the outdoor competition venues in the 2022 Winter Olympics. Nine probabilistic benchmark methods are used to compare the performance of the proposed deep ensemble model. The results indicate that the proposed model exhibits the highest forecasting accuracy and the best ability in uncertainty estimation.

Suggested Citation

  • Zhang, Yi-Ming & Wang, Hao, 2023. "Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting," Energy, Elsevier, vol. 278(PA).
  • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012598
    DOI: 10.1016/j.energy.2023.127865
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    References listed on IDEAS

    as
    1. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    2. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    3. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
    4. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    5. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    6. Yu, Enbo & Xu, Guoji & Han, Yan & Li, Yongle, 2022. "An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms," Energy, Elsevier, vol. 256(C).
    7. Liu, Guanjun & Qin, Hui & Shen, Qin & Lyv, Hao & Qu, Yuhua & Fu, Jialong & Liu, Yongqi & Zhou, Jianzhong, 2021. "Probabilistic spatiotemporal solar irradiation forecasting using deep ensembles convolutional shared weight long short-term memory network," Applied Energy, Elsevier, vol. 300(C).
    8. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    9. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    10. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
    11. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    12. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
    13. Kim, Gyeongmin & Hur, Jin, 2021. "Probabilistic modeling of wind energy potential for power grid expansion planning," Energy, Elsevier, vol. 230(C).
    14. Lopes, Francis M. & Conceição, Ricardo & Silva, Hugo G. & Salgado, Rui & Collares-Pereira, Manuel, 2021. "Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants," Renewable Energy, Elsevier, vol. 163(C), pages 755-771.
    15. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    16. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    Full references (including those not matched with items on IDEAS)

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