Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

TL;DR

This paper introduces an adversarial machine learning approach to improve the resilience of load forecasting systems against cyberattacks, balancing robustness and accuracy in electric utilities.

Contribution

It presents a novel AML-based method that enhances cyberattack resilience in load forecasting without relying on outlier detection, validated through simulation.

Findings

Effective resistance to various cyberattack behaviors

Maintains forecasting accuracy under attack conditions

Validated through simulation studies

Abstract

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.