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An inverse gray-box model for transient building load prediction.

Author(s) : BRAUN J. E., CHATURVEDI N.

Type of article: Article

Summary

Lower costs and improved performance of sensors, controllers, and networking is leading to the development of smart building features, such as continuous performance monitoring automated diagnostics, and optimal supervisory control. For some of these applications, it is important to be able to predict transient cooling and heating requirements for the building using inverse models that are trained using on-site data. Existing inverse models for transient building loads range from purely empirical or "black-box" models to purely physical or "white-box" models. Generally, black-box (e.g. neural network) models require a significant amount of training data and may not always reflect the actual physical behaviour, whereas white-box (e.g. finite difference) models require specification of many physical parameters. The paper presents a hybrid or "gray-box" modelling approach that uses a transfer function with parameters that are constrained to satisfy a simple physical representation for energy flows in the building structure.

Details

  • Original title: An inverse gray-box model for transient building load prediction.
  • Record ID : 2003-1958
  • Languages: English
  • Source: HVAC&R Research - vol. 8 - n. 1
  • Publication date: 2002/01
  • Document available for consultation in the library of the IIR headquarters only.

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