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Cyberattack-resilient load forecasting with adaptive robust regression
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DOI：10.1016/j.ijforecast.2021.06.009.png)
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Abstract
En 中文
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.
Keywords:
Cyber security
Power systems
Load forecasting
Robust method
Regression model
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