Publication:
A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting

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dc.citedby1
dc.contributor.authorRazak I.A.W.A.en_US
dc.contributor.authorIbrahim N.N.A.N.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.authorid57208926032en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid25824750400en_US
dc.contributor.authorid8922419700en_US
dc.date.accessioned2023-05-29T07:28:36Z
dc.date.available2023-05-29T07:28:36Z
dc.date.issued2019
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 exhibit low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimization technique of Bacterial Foraging Optimization Algorithm (BFOA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimized LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatile market worldwide. 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-BFOA shows higher forecast accuracy with lower complexity than the existing models. � Universiti Tun Hussein Onn Malaysia Publisher's Office.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.30880/ijie.2019.11.03.024
dc.identifier.epage239
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85075977485
dc.identifier.spage232
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075977485&doi=10.30880%2fijie.2019.11.03.024&partnerID=40&md5=d8613d4835086ebc5e5c31b470ffc71e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24906
dc.identifier.volume11
dc.publisherPenerbit UTHMen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Integrated Engineering
dc.titleA hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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