Publication:
Medium term load forecasting using evolutionary programming-least square support vector machine

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dc.citedby2
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorZakaria Z.en_US
dc.contributor.authorRazak M.A.A.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid56276791800en_US
dc.contributor.authorid57192126317en_US
dc.contributor.authorid57221906825en_US
dc.date.accessioned2023-05-29T06:01:11Z
dc.date.available2023-05-29T06:01:11Z
dc.date.issued2015
dc.description.abstractThis paper presents new intelligent-based technique namely Evolutionary Programming- Least-Square Support Vector Machine (EP-LSSVM) to forecast a medium term load demand. Medium-term electricity load forecasting is a difficult work since the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. Available historical load data are analyzed and appropriate features are selected for the model. Load demand in the year 2008 until 2010 are used for features in combination with day in months and hour in days. There are 3 inputs vectors for this proposed model consists of day, month and year. As for the output, there are 24 outputs vectors for this model which represents the number of hour in a day. In EP-LSSVM, the Radial Basis Function (RBF) Kernel parameters are optimally selected using Evolutionary Programming (EP) optimization technique for accurate prediction. The performance of EP-LSSVM is compared with those obtained from LS-SVM using crossvalidation technique in terms of accuracy. The experimental results show that the proposed approach gives better performance in terms of Mean Absolute Percentage Error (MAPE) and coefficients of determination (R2) for the entire period of prediction. � 2006-2015 Asian Research Publishing Network (ARPN).en_US
dc.description.natureFinalen_US
dc.identifier.epage9905
dc.identifier.issue21
dc.identifier.scopus2-s2.0-84949953826
dc.identifier.spage9899
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84949953826&partnerID=40&md5=20496159e2f14bee2ab3bcb3d96e3fbb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22469
dc.identifier.volume10
dc.publisherAsian Research Publishing Networken_US
dc.sourceScopus
dc.sourcetitleARPN Journal of Engineering and Applied Sciences
dc.titleMedium term load forecasting using evolutionary programming-least square support vector machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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