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Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion

Author

Listed:
  • Zhihao Shang

    (School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

  • Quan Wen

    (School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

  • Yanhua Chen

    (School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

  • Bing Zhou

    (School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

  • Mingliang Xu

    (School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China)

Abstract

As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data.

Suggested Citation

  • Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2881-:d:794030
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    References listed on IDEAS

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    Cited by:

    1. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
    2. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    3. Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.

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