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Prediction of solar irradiance using grey Wolf optimizer least square support vector machine

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dc.citedby9
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorMohamad H.en_US
dc.contributor.authorWahab N.A.en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid36809989400en_US
dc.contributor.authorid35790572400en_US
dc.date.accessioned2023-05-29T07:29:05Z
dc.date.available2023-05-29T07:29:05Z
dc.date.issued2019
dc.description.abstractPrediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer Least Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v17.i1.pp10-17
dc.identifier.epage17
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85073819682
dc.identifier.spage10
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073819682&doi=10.11591%2fijeecs.v17.i1.pp10-17&partnerID=40&md5=88a3cb8018a110223c851e84287772a5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24933
dc.identifier.volume17
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titlePrediction of solar irradiance using grey Wolf optimizer least square support vector machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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