Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method
Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method
复制标题
DOI：
10.1109/pesgm40551.2019.8973804
复制
发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Jieying Jiao;Zefan Tang;Peng Zhang;M. Yue;Chen Chen-Chen;Jun Yan
中科院分区：
文献类型：
--
作者：
Jieying Jiao;Zefan Tang;Peng Zhang;M. Yue;Chen Chen-Chen;Jun Yan
电力系统中的网络攻击可以改变负荷预测模型的输入数据。虽然可以很容易地识别出不遵循规则模式的极端离群值，但其他更精心设计的攻击可能会逃脱检测，并严重影响负载预测。虽然现有的工作主要集中在增强攻击检测，我们提出了一个网络攻击弹性负载预测模型，是基于经典的胡贝尔的强大的统计方法的适应。在一个大规模的模拟研究中，所提出的方法表现得更好，在各种设置的经典方法。
Cyberattacks in power systems can alter load forecasting models' input data. Although extreme outliers that fail to follow regular patterns can be easily identified, other more carefully-designed attacks can escape detection and seriously impact load forecasting. While existing work mainly focuses on enhancing attack detection, we propose a cyberattack-resilient load forecasting model that is based on an adaptation of classic Huber's robust statistical method. In a large-scale simulation study, the proposed method performed better than the classic method in various settings.