Short-term wind power forecasting using nonnegative sparse coding

Yu Zhang, Seung Jun Kim, Georgios B. Giannakis

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

6 Scopus citations

Abstract

State-of-the-art statistical learning techniques are adapted in this contribution for real-time wind power forecasting. Spatio-temporal wind power outputs are modeled as a linear combination of 'few' atoms in a dictionary. By exploiting geographical information of wind farms, a graph Laplacian-based regularizer encourages positive correlation of wind power levels of adjacent farms. Real-time forecasting is achieved by online nonnegative sparse coding with elastic net regularization. The resultant convex optimization problems are efficiently solved using a block coordinate descent solver. Numerical tests on real data corroborate the merits of the proposed approach, which outperforms competitive alternatives in forecasting accuracy.

Original languageEnglish (US)
Title of host publication2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479984282
DOIs
StatePublished - Apr 15 2015
Event2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 - Baltimore, United States
Duration: Mar 18 2015Mar 20 2015

Publication series

Name2015 49th Annual Conference on Information Sciences and Systems, CISS 2015

Other

Other2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
CountryUnited States
CityBaltimore
Period3/18/153/20/15

Fingerprint Dive into the research topics of 'Short-term wind power forecasting using nonnegative sparse coding'. Together they form a unique fingerprint.

Cite this