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A deep learning sequence model based on self-attention and convolution for wind power prediction

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  • Liu, Chien-Liang
  • Chang, Tzu-Yu
  • Yang, Jie-Si
  • Huang, Kai-Bin

Abstract

Renewable energy has garnered significant attention recently due to its sustainable nature and minimal environmental footprint. Among various sources, wind energy emerges as one of the most promising. However, its inherently unpredictable and irregular characteristics pose challenges to forecasting wind power generation. This study introduces a wind power prediction model that employs self-attention to capture long-range relationships and convolutional layers to understand the local temporal dynamics within time-series data. Unlike traditional deep learning sequence models, such as the recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), our method adeptly integrates both global and local insights. We validate the model’s efficacy through experiments on three datasets. The results consistently show our model’s superior performance over alternative methods. Further, we conduct comprehensive experiments to analyze our proposed model.

Suggested Citation

  • Liu, Chien-Liang & Chang, Tzu-Yu & Yang, Jie-Si & Huang, Kai-Bin, 2023. "A deep learning sequence model based on self-attention and convolution for wind power prediction," Renewable Energy, Elsevier, vol. 219(P1).
    • Handle: RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123013149
    DOI: 10.1016/j.renene.2023.119399
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    References listed on IDEAS

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