Short Term Load Forecasting Models in Czech Republic Using Soft Computing Paradigms

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dc.creatorKhan, Muhammad Riaz
dc.creatorAbraham, Ajith
dc.date2004-05-16
dc.date.accessioned2026-07-07T03:21:16Z
dc.date.available2026-07-07T03:21:16Z
dc.descriptionThis paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data for seven years. A comparison of the proposed techniques is presented for predicting 2 day ahead demands for electricity. Simulation results indicate that hybrid fuzzy neural network and radial basis function networks are the best candidates for the analysis and forecasting of electricity demand.
dc.identifierhttps://arxiv.org/abs/cs/0405051
dc.identifierhttp://arxiv.org/abs/cs/0405051
dc.identifierInternational Journal of Knowledge-Based Intelligent Engineering Systems, IOS Press Netherlands, Volume 7, Number 4, pp. 172-179, 2003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32132
dc.subjectArtificial Intelligence
dc.subjectI.2.0
dc.titleShort Term Load Forecasting Models in Czech Republic Using Soft Computing Paradigms
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