Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2239
Título: Prediction of building's temperature using neural networks models
Autor: Ruano, A. E.
Crispim, E. M.
Conceição, Eusébio
Lúcio, M. M. J. R.
Palavras-chave: Temperature prediction
Neural networks
Multi-objective genetic algorithm
Radial basis function networks
Data: 2006
Editora: Elsevier
Citação: Ruano, A. E.; Crispim, E. M.; Conceição, E. Z. E.; Lúcio, M. M. J. R. Prediction of building's temperature using neural networks models, Energy and Buildings, 38, 6, 682-694, 2006.
Resumo: The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. In this paper the design of inside air temperature predictive neural network models, to be used for predictive control of airconditioned systems, is discussed. The use of multi-objective genetic algorithms for designing off-line radial basis function neural network models is detailed. The performance of these data-driven models is compared, favourably, with a multi-node physically based model. Climate and environmental data from a secondary school located in the south of Portugal, collected by a remote data acquisition system, are used to generate the models. By using a sliding window adaptive methodology, the good results obtained off-line are extended throughout the whole year. The use of long-range predictive models for airconditioning systems control is demonstrated, in simulations, achieving a good temperature regulation with important energy savings.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2239
DOI: http://dx.doi.org/10.1016/j.enbuild.2005.09.007
ISSN: 03787788
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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