@inproceedings{2ef2a43449a9436f9cfe17ebce5a1508,
title = "Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method",
abstract = "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.",
keywords = "Cyber security, Huber's robust method, load forecasting, power systems, regression model",
author = "Jieying Jiao and Zefan Tang and Peng Zhang and Meng Yue and Chen Chen and Jun Yan",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",
year = "2019",
month = aug,
doi = "10.1109/PESGM40551.2019.8973804",
language = "English",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",
}