Power load forecasting using extended normalised radial basis function networks

Authors:
V. S. Kodogiannis;M. Amina;J. N. Lygouras
Affiliations:
Computational Intelligence Group, School of Electronics and Computer Science, University of Westminster, London, UK;School of Electronics and Computer Science, University of Westminster, London, UK;Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Venue:
Journal of Computational Methods in Sciences and Engineering
Year:
2011

Citing 4
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Abstract

Neural networks are currently finding practical applications, ranging from 'soft' regulatory control in consumer products, to the accurate modelling of nonlinear systems. Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. In this paper we analyse the problem of short term load forecasting and propose a novel neural network scheme based on the Extended Normalised Radial Basis Function network. The Bayesian Ying Yang Expectation Maximisation algorithm has been used with novel splitting operations to determine a network size and parameter set. The results, utilising data from Eastern Slovakian Energy Board, are then compared with that of an MLP neural network.