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

Keem Siah Yap, Hwa Jen Yap

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)108-112
Number of pages5
JournalNeurocomputing
Volume81
DOIs
Publication statusPublished - 01 Apr 2012

Fingerprint

Holidays
Multi agent systems
Learning systems
Malaysia
Machine Learning
Pattern recognition
Neural networks

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

Cite this

@article{3ae75278e4b8431daf995e7af429519c,
title = "Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy",
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.",
author = "Yap, {Keem Siah} and Yap, {Hwa Jen}",
year = "2012",
month = "4",
day = "1",
doi = "10.1016/j.neucom.2011.12.002",
language = "English",
volume = "81",
pages = "108--112",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy. / Yap, Keem Siah; Yap, Hwa Jen.

In: Neurocomputing, Vol. 81, 01.04.2012, p. 108-112.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy

AU - Yap, Keem Siah

AU - Yap, Hwa Jen

PY - 2012/4/1

Y1 - 2012/4/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84856329064&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856329064&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2011.12.002

DO - 10.1016/j.neucom.2011.12.002

M3 - Article

VL - 81

SP - 108

EP - 112

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -