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Black box modelling of HVAC system : improving the performances of neural networks

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  • Additional Information
    • Contributors:
      Physique et Ingénierie Mathématique pour l'Énergie, l'environnemeNt et le bâtimenT (PIMENT); Université de La Réunion (UR); IBPSA
    • Publication Information:
      CCSD
      IBPSA
    • Publication Date:
      2003
    • Collection:
      Université de la Réunion: HAL
    • Subject Terms:
    • Abstract:
      Proceedigs available at http://www.ibpsa.org/m_bs2003.asp ; International audience ; This paper deals with neural networks modelling of HVAC systems. In order to increase the neural networks performances, a method based on sensitivity analysis is applied. The same technique is also used to compute the relevance of each input. To avoid the prediction errors in dry coil conditions, a metamodel for each capacity is derived from the neural networks. The regression coefficients of the polynomial forms are identified through the use of spectral analysis. These methods based on sensitivity and spectral analysis lead to an optimized neural network model, as regard to its architecture and predictions.
    • Relation:
      info:eu-repo/semantics/altIdentifier/arxiv/1212.5594; ARXIV: 1212.5594
    • Online Access:
      https://hal.science/hal-00768385
      https://hal.science/hal-00768385v1/document
      https://hal.science/hal-00768385v1/file/Submitted-BS03_0347_354.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.14D9991E