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Short term load forecasting using a hybrid neural network

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dc.citedby7
dc.contributor.authorYap K.S.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorLim C.P.en_US
dc.contributor.authorShah M.S.en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid55666579300en_US
dc.contributor.authorid24448270600en_US
dc.date.accessioned2023-12-28T08:57:52Z
dc.date.available2023-12-28T08:57:52Z
dc.date.issued2006
dc.description.abstractShort Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression. � 2006 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4154476
dc.identifier.doi10.1109/PECON.2006.346632
dc.identifier.epage128
dc.identifier.scopus2-s2.0-46249091355
dc.identifier.spage123
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-46249091355&doi=10.1109%2fPECON.2006.346632&partnerID=40&md5=0bed1a1982d1a6902b43d48e8b5bf4dc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29837
dc.pagecount5
dc.sourceScopus
dc.sourcetitleFirst International Power and Energy Conference, (PECon 2006) Proceedings
dc.subjectGaussian adaptive resonance theory
dc.subjectGeneralized regression neural network
dc.subjectLoad forecasting
dc.subjectTime series prediction
dc.subjectArtificial intelligence
dc.subjectCompetition
dc.subjectE-learning
dc.subjectEducation
dc.subjectElectric load forecasting
dc.subjectElectric power systems
dc.subjectFinance
dc.subjectFood processing
dc.subjectForecasting
dc.subjectGaussian distribution
dc.subjectGenetic algorithms
dc.subjectImage classification
dc.subjectInternet
dc.subjectLoads (forces)
dc.subjectRegression analysis
dc.subjectStatistics
dc.subjectTrellis codes
dc.subjectVegetation
dc.subject(PL) properties
dc.subjectAdaptive resonance theory (ART1)
dc.subjectComputational costs
dc.subjectFinancial performances
dc.subjectgaussian
dc.subjectGaussian regression
dc.subjectGeneralized regression neural network (GRMM)
dc.subjectGrid operations
dc.subjectHybrid neural network (HNN)
dc.subjectInternational (CO)
dc.subjectlearning processes
dc.subjectload demand
dc.subjectOn line learning
dc.subjectpower systems
dc.subjectprediction accuracy
dc.subjectreal values
dc.subjectRegression (R2)
dc.subjectRegression problems
dc.subjectReliability and stability
dc.subjectshort term
dc.subjectShort term load forecasting
dc.subjectShort term load forecasting (STLF)
dc.subjectsimulation results
dc.subjectSupport vector regression (SVR)
dc.subjectTime frames
dc.subjecttraining samples
dc.subjectUnsupervised clustering
dc.subjectUtility companies
dc.subjectNeural networks
dc.titleShort term load forecasting using a hybrid neural networken_US
dc.typeConference paperen_US
dspace.entity.typePublication
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