Load forecasting via low rank plus sparse matrix factorization

Seung Jun Kim, Geogios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Accurate imputation and prediction of load data are important prerequisites for many tasks of power systems, especially as renewables and plug-in electric vehicles penetrate the grid. A low-rank and sparse matrix factorization model is considered for load inference tasks to capture spatial as well as temporal structures in multi-site load data. The low-rank structure captures periodic patterns, and sparse matrix factors explain localized and clustered signatures. In order to predict load values for future time instants (and possibly for unforeseen sites), prior knowledge on correlations is necessarily incorporated in a nonparametric kernel-based learning framework. An efficient learning algorithm is also derived. Tests with real load data verify the efficacy of the proposed approach.

Original languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages1682-1686
Number of pages5
ISBN (Print)9781479923908
DOIs
StatePublished - Jan 1 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

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