An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting

Journal article


Zhao, Shangrui, Wu, Qingyue, Zhang, Yueyi, Wu, Jinran and Li, Xi-An. (2022). An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting. Expert Systems with Applications. 210, p. Article 118467. https://doi.org/10.1016/j.eswa.2022.118467
AuthorsZhao, Shangrui, Wu, Qingyue, Zhang, Yueyi, Wu, Jinran and Li, Xi-An
Abstract

Load forecasting can effectively reduce the operating costs of the power industry, while attacks on the load can lead to inaccurate forecasts. In the existing reports, the robust regression method can potentially alleviate the interference of the attack for load forecasting. However, most current existing methods can handle the data under symmetric attacks, which are not effective in data under asymmetric attacks. In this paper, an asymmetric robust regression method (asymmetric bisquare regression) is proposed for mixed cyberattack-resilient load forecasting. Instead of the symmetric bisquare loss function, in the asymmetric bisquare loss function, two tuning parameters are introduced to control the impacts from negative and positive attacks, respectively. Particularly, the two tuning parameters can be adaptively estimated according to the proportion of attack type. Finally, we demonstrate that the asymmetric robust regression method is superior to all considered robust statistical regression methods through a comparative study of mixed cyberattack-resilient load forecasting.

Keywordsasymmetric loss function; mixed cyberattack; statistical modeling; data-driven method
Year2022
JournalExpert Systems with Applications
Journal citation210, p. Article 118467
PublisherElsevier Ltd
ISSN0957-4174
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eswa.2022.118467
Scopus EID2-s2.0-85136242175
Page range1-15
FunderChinese Fundamental Research Funds for the Central Universities
National Innovation and Entrepreneurship Training Program for College Students, China
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online12 Aug 2022
Publication process dates
Accepted05 Aug 2022
Deposited11 Jul 2023
Grant IDWUT213114009
No. 307
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