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An accurate medium-term load forecasting based on hybrid technique

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dc.citedby2
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorWahab N.A.en_US
dc.contributor.authorRahmat N.A.en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid35790572400en_US
dc.contributor.authorid55647163881en_US
dc.date.accessioned2023-05-29T06:50:56Z
dc.date.available2023-05-29T06:50:56Z
dc.date.issued2018
dc.description.abstractAn accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM. � 2018 Institute of Advanced Engineering and Science All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v12.i1.pp161-167
dc.identifier.epage167
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85051280257
dc.identifier.spage161
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85051280257&doi=10.11591%2fijeecs.v12.i1.pp161-167&partnerID=40&md5=549c114e0048986ccf15fa291a7de7f0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23674
dc.identifier.volume12
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleAn accurate medium-term load forecasting based on hybrid techniqueen_US
dc.typeArticleen_US
dspace.entity.typePublication
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