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Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy

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dc.citedby25
dc.contributor.authorYap K.S.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.date.accessioned2023-12-28T06:30:28Z
dc.date.available2023-12-28T06:30:28Z
dc.date.issued2012
dc.description.abstractIn the previous research, a Multi-Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) has been introduced for solving pattern classification problems. However this model is incapable of handling regression tasks. In this article, a new OSELM-based multi-agent system with weighted average strategy (MAS-OSELM-WA) is introduced for solving data regression tasks. A MAS-OSELM-WA consists of several individual OSELM (individual agent) and the final decision (parent agent). The outputs of the individual agents are sent to the parent agent for a final decision whereby the coefficients of parent agent are computed by a gradient descent method. The effectiveness of the MAS-OSELM-WA is evaluated by an electrical load forecasting problem in Malaysia for a month with consequent national holidays (i.e., during the month of Hari Raya-Malay New Year of Malaysia). The results demonstrated that the MAS-OSELM-WA is able to produce good performance as compared with the other approaches. � 2011 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.neucom.2011.12.002
dc.identifier.epage112
dc.identifier.scopus2-s2.0-84856329064
dc.identifier.spage108
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84856329064&doi=10.1016%2fj.neucom.2011.12.002&partnerID=40&md5=d97476a97e87b046212644b52bff14a4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29547
dc.identifier.volume81
dc.pagecount4
dc.sourceScopus
dc.sourcetitleNeurocomputing
dc.subjectGradient descent
dc.subjectLoad forecasting
dc.subjectMulti-Agent System
dc.subjectOnline Sequential Extreme Learning Machine
dc.subjectWeighted average
dc.subjectE-learning
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectMulti agent systems
dc.subjectNeural networks
dc.subjectStatistical methods
dc.subjectData regression
dc.subjectElectrical load forecasting
dc.subjectFinal decision
dc.subjectGradient descent
dc.subjectGradient Descent method
dc.subjectIndividual agent
dc.subjectLoad forecasting
dc.subjectMalaysia
dc.subjectMaximum load
dc.subjectMulti-agents systems
dc.subjectOnline sequential extreme learning machine
dc.subjectPattern classification problems
dc.subjectWeighted averages
dc.subjectarticle
dc.subjectcorrelation coefficient
dc.subjectdata analysis
dc.subjectforecasting
dc.subjectintermethod comparison
dc.subjectlearning algorithm
dc.subjectmachine learning
dc.subjectMalaysia
dc.subjectmathematical model
dc.subjectonline sequential extreme learning machine
dc.subjectpriority journal
dc.subjectregression analysis
dc.subjectElectric load forecasting
dc.titleDaily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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