Scholarlink

<p>Accurate wind power forecasting is essential for improving renewable energy utilization efficiency and reducing uncertainties and fluctuations in power dispatch. To address this challenge, we propose a hybrid wind power prediction model that integrates a data-driven approach, an attention-based ConvLSTM (ACKLSTM) architecture, and an error correction mechanism. In this framework, the ACKLSTM model incorporates a self-attention (SA) mechanism built upon ConvLSTM and replaces the traditional multilayer perceptron (MLP) with a Kolmogorov-Arnold Network (KAN). Subsequently, a support vector machine (SVM) is employed to correct residual prediction errors, further enhancing forecasting accuracy. The proposed approach is validated through a comprehensive set of experiments. Results demonstrate that with an input sequence length of five time steps, the ACKLSTM model achieves the highest prediction accuracy, attaining an <i>R</i><sup>2</sup> value of 95.70% ± 0.5. The comparative analysis demonstrates that the hybrid model, which integrates error correction and KAN, outperforms alternative methods, achieving the lowest mean squared error (MSE) of 0.091 ± 0.01&#xa0;kW, representing a 70% improvement over the Transformer model. Furthermore, ablation experiments validate the effectiveness of both the error correction technique and the incorporation of KAN in improving model performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An error correction ACKLSTM model combining data-driven and deep learning for wind power prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月20日

查看全文
  • Jianwei Yang,
  • Bowen Zhang,
  • Lin Tong,
  • Shengxian Cao,
  • Bo Zhao,
  • Zhenhao Tang,
  • Gong Wang,
  • Han Gao,
  • Shengyao Sun

摘要

Accurate wind power forecasting is essential for improving renewable energy utilization efficiency and reducing uncertainties and fluctuations in power dispatch. To address this challenge, we propose a hybrid wind power prediction model that integrates a data-driven approach, an attention-based ConvLSTM (ACKLSTM) architecture, and an error correction mechanism. In this framework, the ACKLSTM model incorporates a self-attention (SA) mechanism built upon ConvLSTM and replaces the traditional multilayer perceptron (MLP) with a Kolmogorov-Arnold Network (KAN). Subsequently, a support vector machine (SVM) is employed to correct residual prediction errors, further enhancing forecasting accuracy. The proposed approach is validated through a comprehensive set of experiments. Results demonstrate that with an input sequence length of five time steps, the ACKLSTM model achieves the highest prediction accuracy, attaining an R2 value of 95.70% ± 0.5. The comparative analysis demonstrates that the hybrid model, which integrates error correction and KAN, outperforms alternative methods, achieving the lowest mean squared error (MSE) of 0.091 ± 0.01 kW, representing a 70% improvement over the Transformer model. Furthermore, ablation experiments validate the effectiveness of both the error correction technique and the incorporation of KAN in improving model performance.