Publication:
Malaysian day-type load forecasting

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dc.citedby2
dc.contributor.authorFadhilah A.R.en_US
dc.contributor.authorSuriawati S.en_US
dc.contributor.authorAmir H.H.en_US
dc.contributor.authorIzham Z.A.en_US
dc.contributor.authorMahendran S.en_US
dc.contributor.authorid36988285400en_US
dc.contributor.authorid57224346174en_US
dc.contributor.authorid24447656300en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid23568523100en_US
dc.date.accessioned2023-12-29T07:53:59Z
dc.date.available2023-12-29T07:53:59Z
dc.date.issued2009
dc.description.abstractTime series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. ARMA and REgARMA models are among the times series models considered. ANFIS, a hybrid model from neural network is also discussed as for comparison purposes. The main interest of the forecasts consists of three days up to five days ahead predictions for daily data. The pure autoregressive model with an order 2, or AR (2) with a MAPE value of 1.27% is found to be an appropriate model for forecasting the Malaysian peak daily load for the 3 days ahead prediction. ANFIS model gives a better MAPE value when weekends' data were excluded. Regression models with ARMA errors are found to be good models for forecasting different day types. The selection of these models is depended on the smallest value of AIC statistic and the forecasting accuracy criteria. �2009 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5398613
dc.identifier.doi10.1109/ICEENVIRON.2009.5398613
dc.identifier.epage411
dc.identifier.scopus2-s2.0-77949575977
dc.identifier.spage408
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77949575977&doi=10.1109%2fICEENVIRON.2009.5398613&partnerID=40&md5=3672ad8bb5a16deb0a506acc0592ddd1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30822
dc.pagecount3
dc.relation.ispartofAll Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability
dc.subjectANFIS
dc.subjectARMA
dc.subjectLoad forecasting
dc.subjectMAPE
dc.subjectRegARMA
dc.subjectFuzzy systems
dc.subjectNeural networks
dc.subjectRegression analysis
dc.subjectSustainable development
dc.subjectTime series
dc.subjectTime series analysis
dc.subjectANFIS model
dc.subjectAppropriate models
dc.subjectAuto regressive models
dc.subjectEngineering problems
dc.subjectEnvironmental phenomena
dc.subjectForecasting accuracy
dc.subjectHybrid model
dc.subjectLoad forecasting
dc.subjectMalaysians
dc.subjectOrder 2
dc.subjectRegression model
dc.subjectSystem loads
dc.subjectTimes series
dc.subjectElectric load forecasting
dc.titleMalaysian day-type load forecastingen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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