Dynamic-neural modelling of the thermal behaviour of buildings

  • Authors:

  • R. R. Issa;I. Flood;C. Abi Shdid

  • Affiliations:

  • Rinker School of Building Construction, University of Florida, Gainesville, Florida, United States of America;Rinker School of Building Construction, University of Florida, Gainesville, Florida, United States of America;Rinker School of Building Construction, University of Florida, Gainesville, Florida, United States of America

  • Venue:
  • ICECT'03 Proceedings of the third international conference on Engineering computational technology
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper reports on an on-going research project concerned with developing an alternative approach to simulating the thermal behaviour of residential buildings, based on artificial neural networks (ANNs). The primary objective is to capitalize on the modelling versatility of neural networks to facilitate coarse-grain modelling of complicated composite structures, and to allow design variables to be treated as simple inputs to the model. This approach, in contrast to more conventional modelling approaches (such as the finite element or finite difference methods), enables models to be built quickly, reduces the processing time of a simulation, and allows alternative designs to be evaluated without having to rebuild the model. Such a tool will enable a large number of alternative design decisions to be evaluated within a short period of time, thus allowing an architectural design to be fine-tuned to minimize life cycle costs.