Short term load forecasting with Radial basis function network

Gontar, Z; Hatziargyriou, N

dc.contributor.author	Gontar, Z	en
dc.contributor.author	Hatziargyriou, N	en
dc.date.accessioned	2014-03-01T02:41:59Z
dc.date.available	2014-03-01T02:41:59Z
dc.date.issued	2001	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30717
dc.subject	Neural network	en
dc.subject	Radial Basis Function network	en
dc.subject	Short-Term Load Forecasting	en
dc.subject.other	Distribution companies	en
dc.subject.other	Gaussian basis functions	en
dc.subject.other	Load forecast	en
dc.subject.other	Mean absolute percentage error	en
dc.subject.other	Quasi-newton algorithm	en
dc.subject.other	Regression model	en
dc.subject.other	Short term load forecasting	en
dc.subject.other	Computer simulation	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Experiments	en
dc.subject.other	Neural networks	en
dc.subject.other	Regression analysis	en
dc.subject.other	Radial basis function networks	en
dc.title	Short term load forecasting with Radial basis function network	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/PTC.2001.964939	en
heal.identifier.secondary	http://dx.doi.org/10.1109/PTC.2001.964939	en
heal.identifier.secondary	964939	en
heal.publicationDate	2001	en
heal.abstract	The paper presents experiments with application of Radial Basis Function (RBF) network to Short Term Load Forecasting (STLF) problems. The proposed regression model is used to forecast forty-eight hours ahead electric load. The model has been implemented on real data: inputs to the RBF are past loads, weekday and special-day coding and the output is the load forecast for the given hour. Ordinary RBF was applied in the experiments. The centers of the Gaussian basis functions were selected on the base of the quasi-Newton algorithm. Mean absolute percentage error of about 4% is derived from the data from the Power System in Crete. The performance of the proposed model has been compared with simulations performed by the MLP network, and former models developed for the distribution company in Poland. © 2001 IEEE.	en
heal.journalName	2001 IEEE Porto Power Tech Proceedings	en
dc.identifier.doi	10.1109/PTC.2001.964939	en
dc.identifier.volume	3	en
dc.identifier.spage	372	en
dc.identifier.epage	375	en