A load forecasting hybrid method for an isolated power system

Sideratos, G; Vitellas, I; Hatziargyriou, N

dc.contributor.author	Sideratos, G	en
dc.contributor.author	Vitellas, I	en
dc.contributor.author	Hatziargyriou, N	en
dc.date.accessioned	2014-03-01T02:52:50Z
dc.date.available	2014-03-01T02:52:50Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36101
dc.subject	Load Forecasting	en
dc.subject	Multilayer Perceptrons	en
dc.subject	Particle Swarm Optimization	en
dc.subject	Radial Basis Function Neural Network	en
dc.subject.other	Generalization ability	en
dc.subject.other	Hybrid method	en
dc.subject.other	Hybrid model	en
dc.subject.other	Isolated power system	en
dc.subject.other	Load demand	en
dc.subject.other	Load forecasting	en
dc.subject.other	Particle swarm	en
dc.subject.other	Particle swarm optimization algorithm	en
dc.subject.other	Radial basis function neural networks	en
dc.subject.other	Self-learning	en
dc.subject.other	Cybernetics	en
dc.subject.other	Forecasting	en
dc.subject.other	Intelligent systems	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Multilayers	en
dc.subject.other	Particle swarm optimization (PSO)	en
dc.subject.other	Pattern recognition systems	en
dc.subject.other	Power transmission	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Electric load forecasting	en
dc.title	A load forecasting hybrid method for an isolated power system	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ISAP.2011.6082190	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ISAP.2011.6082190	en
heal.identifier.secondary	6082190	en
heal.publicationDate	2011	en
heal.abstract	This paper presents a load forecasting hybrid model designed for isolated power systems. The proposed model consists of four modules that estimate initially the future load demand and a combination module. Radial basis function neural networks (RBFNNs) are applied to make the initial predictions and multilayer perceptrons (MLPs) are used to combine them. Emphasis is given to the RBFNNs generalization ability developing a self-learning procedure with the Particle Swarm Optimization (PSO) algorithm. Satisfactory results are obtained after the evaluation in the Crete case study. © 2011 IEEE.	en
heal.journalName	2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011	en
dc.identifier.doi	10.1109/ISAP.2011.6082190	en