Publication:
Malaysian peak daily load forecasting

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dc.citedby2
dc.contributor.authorFadhilah R.Abd.en_US
dc.contributor.authorAmir H.H.en_US
dc.contributor.authorIzham A.Z.en_US
dc.contributor.authorMahendran S.en_US
dc.contributor.authorid36988285400en_US
dc.contributor.authorid24447656300en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid23568523100en_US
dc.date.accessioned2023-12-29T07:53:28Z
dc.date.available2023-12-29T07:53:28Z
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 Regression with ARMA errors 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 seven days ahead predictions for daily data. The objective is to find an appropriate model for forecasting the Malaysian peak daily demand of electricity. The pure autoregressive model with an order 2 or AR (2) has the minimum AIC statistic value compared with other ARMA models. AR (2) model recorded the value for the mean absolute percentage error (MAPE) as 1.27 % for the prediction of 3 days ahead from Jan 1 to 3 , 2005. Besides AR(2) model, Regression model with ARMA errors and ANFIS were found to be among the best forecasting models for weekdays with MAPE value from 0.1% to 3%. �2009 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5442993
dc.identifier.doi10.1109/SCORED.2009.5442993
dc.identifier.epage394
dc.identifier.scopus2-s2.0-77952665108
dc.identifier.spage392
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77952665108&doi=10.1109%2fSCORED.2009.5442993&partnerID=40&md5=a5e57343e60adc99868b0bb3c0219e74
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30798
dc.pagecount2
dc.relation.ispartofAll Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleSCOReD2009 - Proceedings of 2009 IEEE Student Conference on Research and Development
dc.subjectANFIS
dc.subjectARMA
dc.subjectLoad Forecasting
dc.subjectRegARMA
dc.subjectErrors
dc.subjectNeural networks
dc.subjectRegression analysis
dc.subjectTime series
dc.subjectTime series analysis
dc.subjectAppropriate models
dc.subjectARMA model
dc.subjectAuto regressive models
dc.subjectDaily load forecasting
dc.subjectEngineering problems
dc.subjectEnvironmental phenomena
dc.subjectForecasting models
dc.subjectHybrid model
dc.subjectLoad forecasting
dc.subjectMalaysians
dc.subjectMean absolute percentage error
dc.subjectOrder 2
dc.subjectRegression model
dc.subjectSystem loads
dc.subjectTimes series
dc.subjectElectric load forecasting
dc.titleMalaysian peak daily load forecastingen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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