Spatiotemporal load curve data cleansing and imputation via sparsity and low rank

G. Mateos, G. Giannakis
{"title":"Spatiotemporal load curve data cleansing and imputation via sparsity and low rank","authors":"G. Mateos, G. Giannakis","doi":"10.1109/SmartGridComm.2012.6486060","DOIUrl":null,"url":null,"abstract":"The smart grid vision is to build an intelligent power network with an unprecedented level of situational awareness and controllability over its services and infrastructure. This paper advocates statistical inference methods to robustify power monitoring tasks against the outlier effects owing to faulty readings and malicious attacks, as well as against missing data due to privacy concerns and communication errors. In this context, a novel load cleansing and imputation scheme is developed leveraging the low intrinsic-dimensionality of spatiotemporal load profiles and the sparse nature of “bad data.” A robust estimator based on principal components pursuit (PCP) is adopted, which effects a twofold sparsity-promoting regularization through an ℓ1-norm of the outliers, and the nuclear norm of the nominal load profiles. After recasting the non-separable nuclear norm into a form amenable to distributed optimization, a distributed (D-) PCP algorithm is developed to carry out the imputation and cleansing tasks using a network of interconnected smart meters. Computer simulations and tests with real load curve data corroborate the convergence and effectiveness of the novel D-PCP algorithm.","PeriodicalId":143915,"journal":{"name":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SmartGridComm.2012.6486060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The smart grid vision is to build an intelligent power network with an unprecedented level of situational awareness and controllability over its services and infrastructure. This paper advocates statistical inference methods to robustify power monitoring tasks against the outlier effects owing to faulty readings and malicious attacks, as well as against missing data due to privacy concerns and communication errors. In this context, a novel load cleansing and imputation scheme is developed leveraging the low intrinsic-dimensionality of spatiotemporal load profiles and the sparse nature of “bad data.” A robust estimator based on principal components pursuit (PCP) is adopted, which effects a twofold sparsity-promoting regularization through an ℓ1-norm of the outliers, and the nuclear norm of the nominal load profiles. After recasting the non-separable nuclear norm into a form amenable to distributed optimization, a distributed (D-) PCP algorithm is developed to carry out the imputation and cleansing tasks using a network of interconnected smart meters. Computer simulations and tests with real load curve data corroborate the convergence and effectiveness of the novel D-PCP algorithm.
基于稀疏度和低秩的时空负荷曲线数据清理与插值
智能电网的愿景是建立一个对其服务和基础设施具有前所未有的态势感知和可控性的智能电网。本文提倡使用统计推理方法来增强电力监测任务的鲁棒性,防止因错误读取和恶意攻击而产生的异常值效应,以及防止因隐私问题和通信错误而导致的数据丢失。在这种背景下,一种新的负载清理和输入方案被开发利用的低本征维度的时空负载轮廓和“坏数据”的稀疏性质。采用基于主成分追踪(PCP)的鲁棒估计,通过离群值的1范数和标称负荷曲线的核范数实现二次稀疏化正则化。在将不可分离核规范重新转换为适合分布式优化的形式后,开发了分布式(D-) PCP算法,使用互联智能电表网络执行输入和清理任务。计算机仿真和实测负载曲线数据验证了D-PCP算法的收敛性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信