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A low data requirement model of a variable-speed vapour compression refrigeration system based on neural networks.

Modèle nécessitant peu de données d'un système frigorifique à compression de vapeur, fondé sur des réseaux neuronaux.

Auteurs : NAVARRO-ESBRÍ J., BERBEGALL V., VERDU G., et al.

Type d'article : Article, Article de la RIF

Résumé

In this work, a model of a vapour compression refrigeration system with a variable-speed compressor, based on a black-box modelling technique, is presented. The kernel of the model consists of a full customized radial basis function network, which has been developed to accurately predict the performance of the system with low cost data requirement in terms of input variables and training data. The work also presents a steady state validation of the model inside and outside the training data set, finding, in both cases, a good agreement between experimental values and those predicted by the model. These results constitute a first step to go through future research on fault detection and energy optimization in variable-speed refrigeration systems.

Documents disponibles

Format PDF

Pages : 1452-1459

Disponible

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    15 €

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    Gratuit

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Détails

  • Titre original : A low data requirement model of a variable-speed vapour compression refrigeration system based on neural networks.
  • Identifiant de la fiche : 2008-0111
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 30 - n. 8
  • Date d'édition : 12/2007

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