Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2142
Título: Improving the identification of RBF predictive models to forecast the Portuguese electricity consumption
Autor: Ferreira, P. M.
Ruano, A. E.
Pestana, Rui
Palavras-chave: Electricity load demand
Radial basis functions
Neural networks
Prediction
Modelling
Data: 2010
Editora: Elsevier, IFAC
Citação: Ferreira, P. M.; Ruano, A. E. Pestana, Rui. Improving the identification of RBF predictive models to forecast the Portuguese electricity consumption, Trabalho apresentado em Control Methodologies and Technology for Energy Efficiency, In IFAC Conference on Control Methodologies and Technology for Energy Efficiency (2010), Vilamoura, 2010.
Resumo: Abstract The Portuguese power grid company wants to improve the accuracy of the electricity load demand forecast within an horizon of 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present updated results on the identi cation of radial basis function neural network load demand predictive models. 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. The set of goals and objectives used in the model optimisation re ect di erent 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. In this work the prediction horizon was increased, the model tness assessment was altered, and the model structure search space was enlarged. Results are also presented for a predictive nearest neighbour type approach, which establishes a baseline for predictive methods comparison.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2142
ISBN: 9783902661685
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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