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<p>In recent years, the demand for renewable energy, particularly wind power, has surged as a sustainable solution to address energy needs while minimizing environmental impact. Accurate wind power forecasting is essential for grid operators, energy traders, and stakeholders to manage output fluctuations, optimize energy generation, and maintain grid stability. The use of Artificial Intelligence and Machine Learning has transformed wind power prediction by facilitating more in-depth analysis of complex data sets. Deep learning models, in particular, have shown remarkable capability in capturing intricate wind patterns, ushering in a new era in wind power forecasting. However, challenges persist due to wind’s inherent variability, complex terrain influences, and reliance on meteorological models, which may introduce errors. In this study, we have proposed a novel hybrid approach incorporating TCN (Temporal Convolutional Neural Networks), Attention, LSTM (Long Short Term Memory), Gating and Dense layer mechanisms to improve wind power output prediction. Furthermore, Adam optimizer is used to boost the model performance. Our novel hybrid model, after a comprehensive comparative analysis with traditional deep learning methods, demonstrates superiority in wind power forecasting, affirming its effectiveness. This innovative approach not only advances current wind power forecasting but also lays a foundation for future research in the field.</p>

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Harnessing Tc-Ls-Ga-At: a novel deep learning based hybrid approach for wind power forecasting

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Kartik Bansal,
  • Kartik Bisht,
  • Priya Singh

摘要

In recent years, the demand for renewable energy, particularly wind power, has surged as a sustainable solution to address energy needs while minimizing environmental impact. Accurate wind power forecasting is essential for grid operators, energy traders, and stakeholders to manage output fluctuations, optimize energy generation, and maintain grid stability. The use of Artificial Intelligence and Machine Learning has transformed wind power prediction by facilitating more in-depth analysis of complex data sets. Deep learning models, in particular, have shown remarkable capability in capturing intricate wind patterns, ushering in a new era in wind power forecasting. However, challenges persist due to wind’s inherent variability, complex terrain influences, and reliance on meteorological models, which may introduce errors. In this study, we have proposed a novel hybrid approach incorporating TCN (Temporal Convolutional Neural Networks), Attention, LSTM (Long Short Term Memory), Gating and Dense layer mechanisms to improve wind power output prediction. Furthermore, Adam optimizer is used to boost the model performance. Our novel hybrid model, after a comprehensive comparative analysis with traditional deep learning methods, demonstrates superiority in wind power forecasting, affirming its effectiveness. This innovative approach not only advances current wind power forecasting but also lays a foundation for future research in the field.