Artificial Neural Networks for Energy Management System Applicability and Limitations of the Main Paradigms

Gonzalo Joya (), Francisco Garcìa-Lagos (), Miguel A. Atencia () and Francisco Sandoval ()
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Gonzalo Joya: Universidad de Málaga
Francisco Garcìa-Lagos: Universidad de Málaga
Miguel A. Atencia: Universidad de Málaga
Francisco Sandoval: Universidad de Málaga

European Journal of Economic and Social Systems, 2004, vol. 17, issue 1-2, 11-28

Abstract: The practical applicability and limitations of the main neural paradigms is revised by studying three main questions: when can a particular problem be approached by means of Artificial Neural Networks? Which is the most suitable neural paradigm for a particular problem? How must the available information be presented to the implemented network? As a case of study, three operations involved in an Energy Management System, instances of general problems often solved by neural networks, have been considered: Load Forecasting, State Estimation and Contingency Analysis. The analysis of the attempted neural solutions brings to light the features and limitations of the main neural paradigms, such as Multilayer Perceptrons with Backpropagation, Radial Basis Function networks, Hopfield networks and Self-Organising Maps.

Keywords: Artificial Neuronal Network; Energy Management System; Neural Paradigms Applicability; Input Selection (search for similar items in EconPapers)
JEL-codes: C45 (search for similar items in EconPapers)
Date: 2004
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