Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/4782
Título: Forecasting the Portuguese electricity consumption using least-squares support vector machines
Autor: Ferreira, P. M.
Cuambe, D. I.
Ruano, A. E.
Pestana, Rui
Palavras-chave: Real-time aspects of intelligent control
Training and adaptation algorithms
Constructive algorithms
Structures for computational intelligence
Design methodologies
Data: 2013
Editora: Elsevier, IFAC
Citação: Ferreira, P.M.; Cuambe, D. I.; Ruano, A. E.; Pestana, Rui. Forecasting the Portuguese Electricity Consumption Using Least-Squares Support Vector Machines, Trabalho apresentado em Intelligent Control and Automation Science, In 3rd IFAC International Conference on Intelligent Control and Automation Science (2013), Chengdu, 2013.
Resumo: The subject of this paper is the multi-step prediction of the Portuguese electricity consumption profile up to a 48-hour prediction horizon. In previous work on this subject, the authors have identified a radial basis function neural network one-step-ahead predictive model, which provides very good prediction accuracy and is currently in use at the Portuguese power-grid company. As the model is a static mapping employing external dynamics and the electricity consumption time-series trend and dynamics are varying with time, further work was carried out in order to test model resetting techniques as a means to update the model over time. In on-line operation, when performing the model reset procedure, it is not possible to employ the same model selection criteria as used in the MOGA identification, which results in the possibility of degraded model performance after the reset operation. This work aims to overcome that undesirable behaviour by means of least-squares support vector machines. Results are presented on the identification of such model by selecting appropriate regression window size and regressor dimension, and on the optimization of the model hyper-parameters. A strategy to update this model over time is also tested and its performance compared to that of the existing neural model. A method to initialize the hyper-parameters is proposed which avoids employing multiple random initialization trials or grid search procedures, and achieves performance above average.
Peer review: yes
URI: http://hdl.handle.net/10400.1/4782
DOI: http://dx.doi.org/ 10.3182/20130902-3-CN-3020.00138
ISBN: 9783902823458
Versão do Editor: http://www.ifac-papersonline.net/Detailed/63493.html
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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