Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2196
Título: Evolving RBF predictive models to forecast the Portuguese electricity consumption
Autor: Ferreira, P. M.
Ruano, A. E.
Pestana, Rui
Kóczy, László T.
Palavras-chave: Electricity load Demand
Radial Basis Functions
Neural Networks
Prediction
Modelling
Data: 2009
Editora: Elsevier, IFAC
Citação: Ferreira, P. M.; Ruano, A. E. Evolving RBF predictive models to forecast the Portuguese electricity consumption, Trabalho apresentado em Intelligent Control Systems and Signal Processing, In Proceedings of the 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing (2009), Istambul, 2009.
Resumo: The Portuguese power grid company wants to improve the accuracy of the electricity load demand (ELD) forecast within an horizon of 24 to 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present some preliminary results about the identi cation of radial basis function (RBF) neural network (NN) ELD predictive models and about the performance of a model selection algorithm. The methodology follows the principles already employed by the authors in di erent applications: the NN models are trained by the Levenberg-Marquardt algorithm using a modi ed training criterion, and the model structure (number of neurons and input terms) is evolved using a Multi-Objective Genetic Algorithm (MOGA). The set of goals and objectives used in the MOGA model optimisation reflect different requirements in the design: obtaining good generalisation ability, good balance between one-step-ahead prediction accuracy and model complexity, and good multi-step prediction accuracy. A number of experiments were carried out, whose results are presented, producing already a number of models whose predictive performance is satisfactory.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2196
DOI: http://dx.doi.org/10.3182/20090921-3-TR-3005.00073
ISBN: 9783902661661
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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