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Conditionnement d'air : modèle innovant de prévision de la charge fondé sur un réseau neuronal biétagé muni de correction de l'erreur résiduelle.

An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction.

Auteurs : YAO Y., LIAN Z., HOU Z., et al.

Type d'article : Article, Article de la RIF

Résumé

Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. They have developed many forecasting methods, such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), grey model (GM) and artificial neural network (ANN), in the field of air-conditioning load prediction. However, none of them has enough accuracy to satisfy the practical demand. On the basis of these models, a novel forecasting method, called 'RBF neural network (RBFNN) with combined residual error correction', is developed in this paper. The new model adopts the advanced algorithm of neural network based on radial basis functions for the air-conditioning load forecasting, and uses the combined forecasting model, which is the combination of MLR, ARIMA and GM, to estimate the residual errors and correct the ultimate foresting results. A study case indicates that RBFNN with combined residual error correction has a much better forecasting accuracy than RBFNN itself and RBFNN with single-model correction.

Documents disponibles

Format PDF

Pages : 528-538

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

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Détails

  • Titre original : An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction.
  • Identifiant de la fiche : 2006-1911
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 29 - n. 4
  • Date d'édition : 06/2006

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