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Long-term load forecasting using grey wolf optimizer-least-squares support vector machine

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dc.citedby3
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorAli Y.M.en_US
dc.contributor.authorMohamad H.en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid57215422906en_US
dc.contributor.authorid36809989400en_US
dc.date.accessioned2023-05-29T08:07:49Z
dc.date.available2023-05-29T08:07:49Z
dc.date.issued2020
dc.description.abstractLong term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer � Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods. � 2020, Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijai.v9.i3.pp417-423
dc.identifier.epage423
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85086913660
dc.identifier.spage417
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086913660&doi=10.11591%2fijai.v9.i3.pp417-423&partnerID=40&md5=440b480ce687fa70f19792f424045c85
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25279
dc.identifier.volume9
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIAES International Journal of Artificial Intelligence
dc.titleLong-term load forecasting using grey wolf optimizer-least-squares support vector machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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