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

Zhe Dong, Yiyang Zhao, Anqi Wang () and Meng Zhou
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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

Energies, 2025, vol. 18, issue 5, 1-22

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.

Keywords: renewable energy sources; wind power prediction; transformer; Mamba (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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