The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adversarial attacks. Although most of the literature focuses on integrity-based attacks, this paper proposes availability-based adversarial attacks, which can be more easily implemented by attackers. For each forecast instance, the availability attack position is optimally solved by mixed-integer reformulation of the artificial neural network. To tackle this attack, an adversarial training algorithm is proposed. In simulation, a realistic load forecasting dataset is considered and the attack performance is compared to the integrity-based attack. Meanwhile, the adversarial training algorithm is shown to significantly improve robustness against availability attacks. All codes are available at https://github.com/xuwkk/AAA_Load_Forecast.
翻译:电力载荷的预测对于电力系统的规划和运行至关重要。最近,深层学习的进步使得能够作出更准确的预测。然而,深神经网络容易发生对抗性攻击。虽然大多数文献都侧重于基于完整性的攻击,但本文建议以可用性为基础的对抗性攻击,攻击者可以更容易地实施。对于每个预测,可用性攻击位置通过人工神经网络的混合整形来最佳地解决。为了应对这一攻击,提议了一个对抗性训练算法。在模拟中,考虑一个现实的载荷预测数据集,攻击性能与基于完整性的攻击相比较。与此同时,对抗性训练算法显示,对可用性攻击的稳健性显著提高。所有代码都可以在 https://github.com/xuwkk/AAA_Load_Forecast上查阅。