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A novel method of BFOA-LSSVM for electricity price forecasting

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dc.citedby1
dc.contributor.authorRazak I.A.W.A.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorAbidin A.A.Z.en_US
dc.contributor.authorRahman T.K.A.en_US
dc.contributor.authorAhmad A.en_US
dc.contributor.authorid56602467500en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid25824750400en_US
dc.contributor.authorid8922419700en_US
dc.contributor.authorid55336187300en_US
dc.date.accessioned2023-05-29T06:11:57Z
dc.date.available2023-05-29T06:11:57Z
dc.date.issued2016
dc.description.abstractForecasting price has now become an essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increase the utilitys profit and energy efficiency. This paper proposes a novel method of Least Square Support Vector Machine (LSSVM) with Bacterial Foraging Optimization Algorithm (BFOA) to predict daily electricity prices in Ontario. The selection of input data and LSSVM's parameters held by BFOA are proven to improve accuracy as well as efficiency of prediction. A comparative study of the proposed method with previous researches was conducted in term of forecast accuracy. The results indicate that (1) the LSSVM with BFOA outperforms other methods for same test data; (2) the optimization algorithm of BFOA gives better accuracy than other optimization techniques. In fact, the proposed approach is less complex compared to other methods presented in this paper. � 2006-2016 Asian Research Publishing Network (ARPN).en_US
dc.description.natureFinalen_US
dc.identifier.epage4968
dc.identifier.issue8
dc.identifier.scopus2-s2.0-84965081612
dc.identifier.spage4961
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84965081612&partnerID=40&md5=ae145de1c00c1df3262dc7f868bc6354
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22740
dc.identifier.volume11
dc.publisherAsian Research Publishing Networken_US
dc.sourceScopus
dc.sourcetitleARPN Journal of Engineering and Applied Sciences
dc.titleA novel method of BFOA-LSSVM for electricity price forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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