Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method

Jieying Jiao; Zefan Tang; Peng Zhang; Meng Yue; Chen Chen; Jun Yan

doi:10.1109/PESGM40551.2019.8973804

Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method

Jieying Jiao
, Zefan Tang
, Peng Zhang
, Meng Yue
, Chen Chen
, Jun Yan

University of Connecticut

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

7 引用（Scopus）

摘要

Cyberattacks in power systems can alter load forecasting models' input data. Although extreme outliers that fail to follow regular patterns can be easily identified, other more carefully-designed attacks can escape detection and seriously impact load forecasting. While existing work mainly focuses on enhancing attack detection, we propose a cyberattack-resilient load forecasting model that is based on an adaptation of classic Huber's robust statistical method. In a large-scale simulation study, the proposed method performed better than the classic method in various settings.

源语言	英语
主期刊名	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
出版商	IEEE Computer Society
ISBN（电子版）	9781728119816
DOI	https://doi.org/10.1109/PESGM40551.2019.8973804
出版状态	已出版 - 8月 2019
已对外发布	是
活动	2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, 美国期限: 4 8月 2019 → 8 8月 2019

出版系列

姓名	IEEE Power and Energy Society General Meeting
卷	2019-August
ISSN（印刷版）	1944-9925
ISSN（电子版）	1944-9933

会议

会议	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
国家/地区	美国
市	Atlanta
时期	4/08/19 → 8/08/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/PESGM40551.2019.8973804

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{e9011c9dcaa145cfa22e66249e01b9c8,

title = "Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method",

abstract = "Cyberattacks in power systems can alter load forecasting models' input data. Although extreme outliers that fail to follow regular patterns can be easily identified, other more carefully-designed attacks can escape detection and seriously impact load forecasting. While existing work mainly focuses on enhancing attack detection, we propose a cyberattack-resilient load forecasting model that is based on an adaptation of classic Huber's robust statistical method. In a large-scale simulation study, the proposed method performed better than the classic method in various settings.",

keywords = "Cyber security, Huber's robust method, load forecasting, power systems, regression model",

author = "Jieying Jiao and Zefan Tang and Peng Zhang and Meng Yue and Chen Chen and Jun Yan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",

year = "2019",

month = aug,

doi = "10.1109/PESGM40551.2019.8973804",

language = "英语",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",

}

Jiao, J, Tang, Z, Zhang, P, Yue, M, Chen, C & Yan, J 2019, Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method. 在 2019 IEEE Power and Energy Society General Meeting, PESGM 2019., 8973804, IEEE Power and Energy Society General Meeting, 卷 2019-August, IEEE Computer Society, 2019 IEEE Power and Energy Society General Meeting, PESGM 2019, Atlanta, 美国, 4/08/19. https://doi.org/10.1109/PESGM40551.2019.8973804

Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method. / Jiao, Jieying; Tang, Zefan; Zhang, Peng 等.
2019 IEEE Power and Energy Society General Meeting, PESGM 2019. IEEE Computer Society, 2019. 8973804 (IEEE Power and Energy Society General Meeting; 卷 2019-August).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method

AU - Jiao, Jieying

AU - Tang, Zefan

AU - Zhang, Peng

AU - Yue, Meng

AU - Chen, Chen

AU - Yan, Jun

PY - 2019/8

Y1 - 2019/8

N2 - Cyberattacks in power systems can alter load forecasting models' input data. Although extreme outliers that fail to follow regular patterns can be easily identified, other more carefully-designed attacks can escape detection and seriously impact load forecasting. While existing work mainly focuses on enhancing attack detection, we propose a cyberattack-resilient load forecasting model that is based on an adaptation of classic Huber's robust statistical method. In a large-scale simulation study, the proposed method performed better than the classic method in various settings.

AB - Cyberattacks in power systems can alter load forecasting models' input data. Although extreme outliers that fail to follow regular patterns can be easily identified, other more carefully-designed attacks can escape detection and seriously impact load forecasting. While existing work mainly focuses on enhancing attack detection, we propose a cyberattack-resilient load forecasting model that is based on an adaptation of classic Huber's robust statistical method. In a large-scale simulation study, the proposed method performed better than the classic method in various settings.

KW - Cyber security

KW - Huber's robust method

KW - load forecasting

KW - power systems

KW - regression model

UR - https://www.scopus.com/pages/publications/85079065995

U2 - 10.1109/PESGM40551.2019.8973804

DO - 10.1109/PESGM40551.2019.8973804

M3 - 会议稿件

AN - SCOPUS:85079065995

T3 - IEEE Power and Energy Society General Meeting

BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

PB - IEEE Computer Society

T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

Y2 - 4 August 2019 through 8 August 2019

ER -

Ensuring Cyberattack-Resilient Load Forecasting with A Robust Statistical Method

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此