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Wind-Mambaformer: Ultra-Short-Term Wind Turbine Power Forecasting Based on Advanced Transformer and Mamba Models

Author

Listed:
  • Zhe Dong

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Yiyang Zhao

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Anqi Wang

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Meng Zhou

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

Abstract

As global climate change accelerates and fossil fuel reserves dwindle, renewable energy sources, especially wind energy, are progressively emerging as the primary means for electricity generation. Yet, wind energy’s inherent stochasticity and uncertainty present significant challenges, impeding its wider application. Consequently, precise prediction of wind turbine power generation becomes crucial. This paper introduces a novel wind power prediction model, the Wind-Mambaformer, which leverages the Transformer framework, with unique modifications to overcome the adaptability limitations faced by traditional wind power prediction models. It embeds Flow-Attention with Mamba to effectively address issues related to high computational complexity, weak time-series prediction, and poor model adaptation in ultra-short-term wind power prediction tasks. This can help to greatly optimize the operation and dispatch of power systems. The Wind-Mambaformer model not only boosts the model’s capability to extract temporal features but also minimizes computational demands. Experimental results highlight the exceptional performance of the Wind-Mambaformer across a variety of wind farms. Compared to the standard Transformer model, our model achieves a remarkable reduction in mean absolute error (MAE) by approximately 30% and mean square error (MSE) by nearly 60% across all datasets. Moreover, a series of ablation experiments further confirm the soundness of the model design.

Suggested Citation

  • Zhe Dong & Yiyang Zhao & Anqi Wang & Meng Zhou, 2025. "Wind-Mambaformer: Ultra-Short-Term Wind Turbine Power Forecasting Based on Advanced Transformer and Mamba Models," Energies, MDPI, vol. 18(5), pages 1-22, February.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1155-:d:1600496
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    References listed on IDEAS

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