In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In this paper, we propose an adversarial machine learning (AML) approach, which can respond to a wide range of attack behaviors without detecting outliers. It strikes a balance between enhancing a system's robustness against cyberattacks and maintaining a reasonable degree of forecasting accuracy when there is no attack. Attack models and configurations for the adversarial training were selected and evaluated to achieve the desired level of performance in a simulation study. The results validate the effectiveness and excellent performance of the proposed method.
@article{arxiv.2001.02289,
title = {Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning},
author = {Zefan Tang and Jieying Jiao and Peng Zhang and Meng Yue and Chen Chen and Jun Yan},
journal= {arXiv preprint arXiv:2001.02289},
year = {2020}
}