Journal
INTERNATIONAL JOURNAL OF FORECASTING
Volume 38, Issue 3, Pages 910-919Publisher
ELSEVIER
DOI: 10.1016/j.ijforecast.2021.06.009
Keywords
Cyber security; Power systems; Load forecasting; Robust method; Regression model
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This paper proposes a cyberattack-resilient load forecasting approach based on an adaptive robust regression method, where the observations are trimmed based on their residuals. Comparison study shows that the proposed method outperforms the standard robust regression in various settings.
Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings. (C) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
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