Adaptive gaussian process for short-term wind speed forecasting

Xiaoqian Jiang, Bing Dong, Le Xie, Latanya Sweeney

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

23 Scopus citations

Abstract

We study the problem of short term wind speed prediction, which is a critical factor for effective wind power generation. This is a challenging task due to the complex and stochastic behavior of the wind environment. Observing various periods in the wind speed time series present different patterns, we suggest a nonlinear adaptive framework to model various hidden dynamic processes. The model is essentially data driven, which leverages non-parametric Heteroscdastic Gaussian Process to model relevant patterns for short term prediction. We evaluate our model on two different real world wind speed datasets from National Data Buoy Center. We compare our results to state-of-arts algorithms to show improvement in terms of both Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).

Original languageEnglish (US)
Title of host publicationECAI 2010
PublisherIOS Press
Pages661-666
Number of pages6
ISBN (Print)9781607506058
DOIs
StatePublished - Jan 1 2010
Externally publishedYes
Event2nd Workshop on Knowledge Representation for Health Care, KR4HC 2010, held in conjunction with the 19th European Conference in Artificial Intelligence, ECAI 2010 - Lisbon, Portugal
Duration: Aug 17 2010Aug 17 2010

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume215
ISSN (Print)0922-6389

Conference

Conference2nd Workshop on Knowledge Representation for Health Care, KR4HC 2010, held in conjunction with the 19th European Conference in Artificial Intelligence, ECAI 2010
CountryPortugal
CityLisbon
Period8/17/108/17/10

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Jiang, X., Dong, B., Xie, L., & Sweeney, L. (2010). Adaptive gaussian process for short-term wind speed forecasting. In ECAI 2010 (pp. 661-666). (Frontiers in Artificial Intelligence and Applications; Vol. 215). IOS Press. https://doi.org/10.3233/978-1-60750-606-5-661