Authors: Jiaming Li, Geoff Poulton, Glenn Platt, Josh Wall, Geoff James
Addresses: CSIRO ICT Centre, PO Box 76, Epping, NSW 1710, Australia. ' CSIRO ICT Centre, PO Box 76, Epping, NSW 1710, Australia. ' CSIRO Energy Technology, PO Box 330, Newcastle NSW 2300, Australia. ' CSIRO Energy Technology, PO Box 330, Newcastle NSW 2300, Australia. ' CSIRO Energy Technology, PO Box 330, Newcastle NSW 2300, Australia
Abstract: This paper presents the development and validation of a dynamic zone model used for improved control of a heating, ventilation and air conditioning (HVAC) system to reduce energy consumption and improve the quality of the indoor environment. In particular, the paper focuses on a zone modelling technique that uses physical-principles based real-time model fitting and prediction methodology, taking advantage of genetic algorithm based problem solving. An air-conditioning zone model is deduced from an energy and mass balance and then expressed in terms of electric circuit theory, where the electric circuit is used to represent functions of the building elements. Experimental results for real-time zone model fitting and prediction are given. The results show that our model is capable of accurately predicting the indoor temperature of a dynamic zone. This dynamic model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.
Keywords: dynamic zones; dynamic modelling; model fitting; room modelling; genetic algorithms; GA; temperature prediction; HVAC control; heating; ventilation; air conditioning; energy consumption; indoor environment quality.
International Journal of Modelling, Identification and Control, 2010 Vol.9 No.1/2, pp.5 - 14
Published online: 01 Apr 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article