Publication:
Moving holidays' effects on the Malaysian peak daily load

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dc.citedby7
dc.contributor.authorRazak F.Abd.en_US
dc.contributor.authorHashim A.H.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorShitan M.en_US
dc.contributor.authorid36988285400en_US
dc.contributor.authorid24447656300en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid23568523100en_US
dc.date.accessioned2023-12-29T07:49:41Z
dc.date.available2023-12-29T07:49:41Z
dc.date.issued2010
dc.description.abstractMalaysia's yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short -, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaike's information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Year's Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia. �2010 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5697708
dc.identifier.doi10.1109/PECON.2010.5697708
dc.identifier.epage910
dc.identifier.scopus2-s2.0-79951793862
dc.identifier.spage906
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79951793862&doi=10.1109%2fPECON.2010.5697708&partnerID=40&md5=44287780036eda39103bc70d853af05e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30576
dc.pagecount4
dc.relation.ispartofAll Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitlePECon2010 - 2010 IEEE International Conference on Power and Energy
dc.subjectARMA
dc.subjectDynamic regression
dc.subjectMAPE
dc.subjectSARIMA
dc.subjectTransfer function
dc.subjectAutocorrelation
dc.subjectRegression analysis
dc.subjectTime series analysis
dc.subjectTransfer functions
dc.subjectAkaike's information criterions
dc.subjectAppropriate models
dc.subjectARMA
dc.subjectAutocorrelation functions
dc.subjectChinese New Year
dc.subjectChristmas
dc.subjectDecision makers
dc.subjectElectricity-consumption
dc.subjectForecasting accuracy
dc.subjectForecasting error
dc.subjectFunction modelling
dc.subjectLoad forecasting
dc.subjectMalaysia
dc.subjectMalaysians
dc.subjectMAPE
dc.subjectMean absolute percentage error
dc.subjectPartial autocorrelation function
dc.subjectPower utility
dc.subjectPublic holidays
dc.subjectSARIMA
dc.subjectShort term load forecasting
dc.subjectElectric load forecasting
dc.titleMoving holidays' effects on the Malaysian peak daily loaden_US
dc.typeConference paperen_US
dspace.entity.typePublication
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