Structure of the forecasting model.

Deep neural networks (DNNs) have achieved remarkable success in wind power forecasting, but DNNs are vulnerable to adversarial attacks that can severely degrade forecast accuracy. Existing studies primarily emphasize attack effectiveness and pay limited attention to attack stealthiness. In this paper, a dimension-constrained momentum iterative fast gradient sign method (DC-MI-FGSM) is proposed for wind power forecasting, which generates highly stealthy perturbations by applying the momentum update mechanism during attack optimization and limiting the perturbation dimensions of input samples. To defend against this attack, a denoising autoencoder (DAE)-based preprocessing defense strategy is developed for wind power forecasting, which resists adversarial attacks by mapping adversarial samples back to their corresponding clean forms. The effectiveness of the proposed attack and defense methods is validated on the public SDWPF dataset under both white-box and black-box scenarios. Compared with existing attacks, DC-MI-FGSM achieves a lower average perturbation percentage (APP), indicating superior attack stealthiness. Meanwhile, it causes more severe degradation in forecasting accuracy, as measured by MAPE, RMSE, and MAE, demonstrating stronger attack effectiveness. For defense, the proposed DAE-based preprocessing strategy effectively mitigates adversarial perturbations, significantly reducing forecasting errors while preserving the original accuracy on clean data. Moreover, it consistently outperforms adversarial training in terms of robustness and usability.

深度神经网络（Deep Neural Networks，DNNs）在风电功率预测领域已取得显著成效，但这类网络极易受到对抗性攻击，进而严重降低预测精度。现有研究主要聚焦于攻击的有效性，却对攻击的隐蔽性关注不足。本文针对风电功率预测场景，提出一种维度约束动量迭代快速梯度符号法（Dimension-Constrained Momentum Iterative Fast Gradient Sign Method，DC-MI-FGSM）：该方法在攻击优化过程中引入动量更新机制，并限制输入样本的扰动维度，从而生成隐蔽性极强的对抗扰动。为抵御此类攻击，本文同时提出一种基于去噪自编码器（Denoising Autoencoder，DAE）的预处理防御策略，通过将对抗样本映射回其对应的干净样本形式，实现对抗性攻击的抵御。本文在公开SDWPF数据集的白盒与黑盒场景下，对所提攻击与防御方法的有效性进行了验证。与现有攻击方法相比，DC-MI-FGSM的平均扰动百分比（Average Perturbation Percentage，APP）更低，表明其具备更优的攻击隐蔽性；同时，该方法通过平均绝对百分比误差（Mean Absolute Percentage Error，MAPE）、均方根误差（Root Mean Square Error，RMSE）与平均绝对误差（Mean Absolute Error，MAE）衡量，可造成更严重的预测精度下降，证明其具备更强的攻击有效性。在防御任务中，本文提出的基于去噪自编码器的预处理策略可有效缓解对抗扰动，在显著降低预测误差的同时，保留了干净样本上的原有预测精度。此外，该策略在鲁棒性与可用性方面始终优于对抗训练。