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Dynamic regression intervention modeling for the Malaysian daily load

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dc.contributor.authorRazak F.A.en_US
dc.contributor.authorShitan M.en_US
dc.contributor.authorHashim A.H.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorid36988285400en_US
dc.contributor.authorid23568523100en_US
dc.contributor.authorid24447656300en_US
dc.contributor.authorid35606640500en_US
dc.date.accessioned2023-05-16T02:47:46Z
dc.date.available2023-05-16T02:47:46Z
dc.date.issued2014
dc.description.abstractMalaysia is a unique country due to having both fixed and moving holidays. These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays' effects in the load forecasting are known to be higher than other factors. If these effects can be estimated and removed, the behavior of the series could be better viewed. Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis. Based on the linear transfer function method, a daily load model consists of either peak or average is developed. The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays' effects and achieved a smaller Mean Absolute Percentage Error (MAPE) in load forecast.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18187/pjsor.v10i1.600
dc.identifier.epage55
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84901489745
dc.identifier.spage41
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84901489745&doi=10.18187%2fpjsor.v10i1.600&partnerID=40&md5=d17aeba4136eea774e9b59de68ce8e1f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22141
dc.identifier.volume10
dc.publisherUniversity of the Punjaben_US
dc.relation.ispartofAll Open Access, Hybrid Gold, Green
dc.sourceScopus
dc.sourcetitlePakistan Journal of Statistics and Operation Research
dc.titleDynamic regression intervention modeling for the Malaysian daily loaden_US
dc.typeArticleen_US
dspace.entity.typePublication
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