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A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting

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dc.citedby12
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.authorNasir M.N.M.en_US
dc.contributor.authorid56602467500en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid25824750400en_US
dc.contributor.authorid8922419700en_US
dc.contributor.authorid55658799800en_US
dc.date.accessioned2023-05-29T06:38:15Z
dc.date.available2023-05-29T06:38:15Z
dc.date.issued2017
dc.descriptionCommerce; Costs; Electric power systems; Forecasting; Genetic algorithms; Optimization; Support vector machines; Accuracy; Electrical power system; Electricity price forecasting; Electricity prices; Least square support vector machines; Multi-stage optimization; Optimized parameter; Parameter selection; Power marketsen_US
dc.description.abstractPredicting price has now become an important task in the operation of electrical power system. Day-ahead prediction provides forecast prices for a day ahead that is useful for daily operation and decision-making. The main challenge for day ahead price forecasting is the accuracy and efficiency. Lower accuracy is produced due to the nature of electricity price that is highly volatile compared to load series. Hence, some researchers have developed complex procedures to produce accurate forecast while considering significant features and optimum parameters. 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 day-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 two 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. � 2016 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7951593
dc.identifier.doi10.1109/PECON.2016.7951593
dc.identifier.epage395
dc.identifier.scopus2-s2.0-85024382225
dc.identifier.spage390
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85024382225&doi=10.1109%2fPECON.2016.7951593&partnerID=40&md5=1bd04f21d281e4dcd007d872da635aa9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23184
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitlePECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding
dc.titleA novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecastingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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