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An hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithm

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dc.citedby3
dc.contributor.authorRazak I.A.W.A.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorSiah Y.K.en_US
dc.contributor.authorAbidin A.A.Z.en_US
dc.contributor.authorRahman T.K.A.en_US
dc.contributor.authorBaharin N.en_US
dc.contributor.authorJali M.H.B.en_US
dc.contributor.authorid56602467500en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid25824750400en_US
dc.contributor.authorid8922419700en_US
dc.contributor.authorid55912740900en_US
dc.contributor.authorid56078350800en_US
dc.date.accessioned2023-05-29T06:52:13Z
dc.date.available2023-05-29T06:52:13Z
dc.date.issued2018
dc.description.abstractPredicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models. � 2018 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v10.i2.pp748-755
dc.identifier.epage755
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85042798521
dc.identifier.spage748
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85042798521&doi=10.11591%2fijeecs.v10.i2.pp748-755&partnerID=40&md5=de06a1fd68c4495a9878adf9dcaf9ce9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23831
dc.identifier.volume10
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Hybrid Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleAn hour ahead electricity price forecasting with least square support vector machine and bacterial foraging optimization algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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