In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.

翻译：近年来，研究者提出了多种用于风电功率预测的深度学习模型。这些模型比传统机器学习算法或物理模型能更准确地预测风电场或整个区域的风电发电量。然而，最新研究表明，深度学习模型往往容易受到对抗攻击的操纵。由于风电功率预测对现代电力系统的稳定性至关重要，保护其免受此类威胁具有重要意义。本文研究了两种不同预测模型在定向、半定向和非定向对抗攻击下的脆弱性。我们采用长短期记忆网络预测单个风电场的发电量，并采用卷积神经网络预测德国全境的风电发电量。此外，我们提出了总对抗鲁棒性评分这一评估指标，用于量化回归模型对定向和半定向对抗攻击的鲁棒性。该指标通过赋予0（极度脆弱）到1（高度鲁棒）之间的评分，评估攻击对模型性能的影响以及攻击者目标的达成程度。实验中，长短期记忆网络预测模型表现出较强的鲁棒性，在所有研究的对抗攻击下总对抗鲁棒性评分均超过0.78。而卷积神经网络预测模型在常规训练下总对抗鲁棒性评分仅低于0.10，因此非常脆弱。然而，通过对抗训练可显著提升其鲁棒性，使其总对抗鲁棒性评分始终高于0.46。