Publication:
Short term electricity price forecasting with multistage optimization technique of LSSVM-GA

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dc.citedby1
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.authorid56602467500en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid25824750400en_US
dc.contributor.authorid8922419700en_US
dc.date.accessioned2023-05-29T06:40:08Z
dc.date.available2023-05-29T06:40:08Z
dc.date.issued2017
dc.description.abstractPrice prediction has now become an important task in the operation of electrical power system. In short term forecast, electricity price can be predicted for an hour-ahead or day-ahead. An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour. It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most of the 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 multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by three stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.en_US
dc.description.natureFinalen_US
dc.identifier.epage122
dc.identifier.issue2-Jul
dc.identifier.scopus2-s2.0-85032900285
dc.identifier.spage117
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85032900285&partnerID=40&md5=00432bb9d01260e295184d5f0038d89c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23397
dc.identifier.volume9
dc.publisherUniversiti Teknikal Malaysia Melakaen_US
dc.sourceScopus
dc.sourcetitleJournal of Telecommunication, Electronic and Computer Engineering
dc.titleShort term electricity price forecasting with multistage optimization technique of LSSVM-GAen_US
dc.typeArticleen_US
dspace.entity.typePublication
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