Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87598
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dc.contributor.authorZhang, Hongen
dc.contributor.authorChen, Lixingen
dc.contributor.authorQu, Yongen
dc.contributor.authorZhao, Guoen
dc.contributor.authorGuo, Zhenweien
dc.date.accessioned2018-12-03T07:00:13Zen
dc.date.accessioned2019-12-06T16:45:20Z-
dc.date.available2018-12-03T07:00:13Zen
dc.date.available2019-12-06T16:45:20Z-
dc.date.issued2014en
dc.identifier.citationZhang, H., Chen, L., Qu, Y., Zhao, G., & Guo, Z. (2014). Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting. Journal of Applied Mathematics, 2014, 835791-. doi:10.1155/2014/835791en
dc.identifier.issn1110-757Xen
dc.identifier.urihttps://hdl.handle.net/10356/87598-
dc.description.abstractThe purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.en
dc.format.extent11 p.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of Applied Mathematicsen
dc.rights© 2014 Hong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectWind Power Forecastingen
dc.subjectSupport Vector Regression (SVR)en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleSupport vector regression based on grid-search method for short-term wind power forecastingen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.organizationVLSI Laben
dc.identifier.doi10.1155/2014/835791en
dc.description.versionPublished versionen
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