Affiliations: [a] Computational Intelligence Group, School of Electronics and Computer Science, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UK 49 Patriarxou Grigoriou E' Str., Heraklion, Crete, GR-71305, Greece | [b] School of Electronics and Computer Science, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UK | [c] Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, GR-67100, Greece | [x] University of Westminster, Westminster, UK | [y] The University of Manchester, Manchester, UK
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.
