Abstract | Neural networks are currently finding practical applications, ranging from ‘soft’ regulatory control in consumer products, to the accurate modelling of nonlinear systems. This paper presents the development of improved neural-network-based short-term electric load forecasting models for the power system of the Greek island of Crete. Several approaches, including radial basis function networks, dynamic neural networks and fuzzy-neural-type networks, have been proposed, and are discussed in this paper. Their performances are evaluated through a simulation study, using metered data provided by the Greek Public Power Corporation. The results indicate that the load-forecasting models developed in this way provide more accurate forecasts, compared with conventional backpropagation network forecasting models. Finally, the embedding of the new model capability in a modular forecasting system is presented. |
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