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<p>The utilisation of wind energy has attracted considerable interest due to its potential as a sustainable and ecologically sound form of power generation. Nevertheless, the accurate prediction of wind speed continues to pose a significant challenge, primarily due to the inherent variability and randomness associated with this natural phenomenon. Transformer-based models have been increasingly employed for wind speed prediction (WSP), yet they encounter challenges in handling past input data redundancies and optimizing encoder-decoder connections. To tackle these issues, a novel hybrid model, SVMD-YFormer-KMSE is presented. The proposed model integrates successive variational mode decomposition (SVMD) with a Y-former model, using the K-MSE loss function. The use of the SVMD component yields enhanced data decomposition and concurrently enhances computing efficiency. SVMD is employed to denoise the input data and then denoised data inputted into the Y-former model to interpret both coarse and fine-grained characteristics for accurate WSP. The Y-former model integrates sparse attention modules to interpret the wind speed efficiently. Moreover, the Y-former model also maintains stability within encoder and decoder stacks through the reconstruction of recent past data, thereby ensuring consistency and reliability in WSP forecasts. The proposed Kernel-MSE loss function effectively captures the nonlinear nature of wind speed data, enhancing model accuracy and convergence. The proposed hybrid model has been extensively evaluated using Leicester and Portland wind farm data, showcasing its resilience and capacity to generalise over different wind speed profiles.</p>

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A novel hybrid framework based on decomposition and Y-former model for accurate wind speed forecasting

  • 期刊论文

  • 发表日期：

    2025年4月14日

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  • Srihari Parri,
  • Kiran Teeparthi,
  • D. M. Vinod Kumar

摘要

The utilisation of wind energy has attracted considerable interest due to its potential as a sustainable and ecologically sound form of power generation. Nevertheless, the accurate prediction of wind speed continues to pose a significant challenge, primarily due to the inherent variability and randomness associated with this natural phenomenon. Transformer-based models have been increasingly employed for wind speed prediction (WSP), yet they encounter challenges in handling past input data redundancies and optimizing encoder-decoder connections. To tackle these issues, a novel hybrid model, SVMD-YFormer-KMSE is presented. The proposed model integrates successive variational mode decomposition (SVMD) with a Y-former model, using the K-MSE loss function. The use of the SVMD component yields enhanced data decomposition and concurrently enhances computing efficiency. SVMD is employed to denoise the input data and then denoised data inputted into the Y-former model to interpret both coarse and fine-grained characteristics for accurate WSP. The Y-former model integrates sparse attention modules to interpret the wind speed efficiently. Moreover, the Y-former model also maintains stability within encoder and decoder stacks through the reconstruction of recent past data, thereby ensuring consistency and reliability in WSP forecasts. The proposed Kernel-MSE loss function effectively captures the nonlinear nature of wind speed data, enhancing model accuracy and convergence. The proposed hybrid model has been extensively evaluated using Leicester and Portland wind farm data, showcasing its resilience and capacity to generalise over different wind speed profiles.