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In the face of an increasingly broad cyberattack surface,cyberattack-resilient load forecasting for electric utilities is both morenecessary and more challenging than ever. In this paper, we propose anadversarial machine learning (AML) approach, which can respond to a wide rangeof attack behaviors without detecting outliers. It strikes a balance betweenenhancing a system's robustness against cyberattacks and maintaining areasonable degree of forecasting accuracy when there is no attack. Attackmodels and configurations for the adversarial training were selected andevaluated to achieve the desired level of performance in a simulation study.The results validate the effectiveness and excellent performance of theproposed method.

Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning