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Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Author

Listed:
  • Kumar Shivam

    (Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang District, Tainan City 710, Taiwan)

  • Jong-Chyuan Tzou

    (Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang District, Tainan City 710, Taiwan)

  • Shang-Chen Wu

    (Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang District, Tainan City 710, Taiwan)

Abstract

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

Suggested Citation

  • Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1772-:d:342383
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    References listed on IDEAS

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    Cited by:

    1. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
    2. Lars Ødegaard Bentsen & Narada Dilp Warakagoda & Roy Stenbro & Paal Engelstad, 2023. "A Unified Graph Formulation for Spatio-Temporal Wind Forecasting," Energies, MDPI, vol. 16(20), pages 1-23, October.
    3. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    4. Bommidi, Bala Saibabu & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function," Energy, Elsevier, vol. 265(C).
    5. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Kui Yang & Bofu Wang & Xiang Qiu & Jiahua Li & Yuze Wang & Yulu Liu, 2022. "Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit," Energies, MDPI, vol. 15(12), pages 1-24, June.
    7. Qin Chen & Yan Chen & Xingzhi Bai, 2020. "Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind," Energies, MDPI, vol. 13(21), pages 1-23, October.

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