Publication:
An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting

Simple item page
dc.citedby2
dc.contributor.authorAbdul Razak I.A.W.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 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.authorid57202805937en_US
dc.date.accessioned2023-05-29T06:56:47Z
dc.date.available2023-05-29T06:56:47Z
dc.date.issued2018
dc.description.abstractPredicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. The monthly average of Hourly Ontario Electricity Price (HOEP) for the past 12 months and month index are selected as the input features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models. � 2018 Universiti Teknikal Malaysia Melaka. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage103
dc.identifier.issue2-May
dc.identifier.scopus2-s2.0-85049394628
dc.identifier.spage99
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85049394628&partnerID=40&md5=25598ff99a690210580a23d0b5c48dc9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24197
dc.identifier.volume10
dc.publisherUniversiti Teknikal Malaysia Melakaen_US
dc.sourceScopus
dc.sourcetitleJournal of Telecommunication, Electronic and Computer Engineering
dc.titleAn optimization method of genetic algorithm for lssvm in medium term electricity price forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS