Publication: Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy
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Date
2012
Authors
Yap K.S.
Yap H.J.
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Abstract
In 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.
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Keywords
Gradient descent , Load forecasting , Multi-Agent System , Online Sequential Extreme Learning Machine , Weighted average , E-learning , Forecasting , Learning systems , Multi agent systems , Neural networks , Statistical methods , Data regression , Electrical load forecasting , Final decision , Gradient descent , Gradient Descent method , Individual agent , Load forecasting , Malaysia , Maximum load , Multi-agents systems , Online sequential extreme learning machine , Pattern classification problems , Weighted averages , article , correlation coefficient , data analysis , forecasting , intermethod comparison , learning algorithm , machine learning , Malaysia , mathematical model , online sequential extreme learning machine , priority journal , regression analysis , Electric load forecasting