The z-transfer function technique is used in calculation for HVAC design and building energy consumption. Theoretical calculation methods to determine the z-transfer function coefficients, which characterize the dynamic thermal performance of building components, do exist, but these depend on a number of assumptions and one must supplement them with experimental techniques. This paper discusses a neural-network-based system identification technique to determine the z-transfer function of a building envelope from experimental data. A multi-layer neural network is trained by the samples constructed from the dynamically measured data of heat conduction process through a wall. The Markov parameters, which are produced from the weighting matrices of the network, are utilized to realize the minimal state space model of the wall by eigensystem realization algorithm. The z-transfer function coefficients are obtained by the algorithm transforming the state space model into z-transfer function. The results show that this technique has some advantages in programming and computational simplicity, very good properties of noise rejection and improved accuracy of the results. The training time of the network is greatly reduced by adopting the adaptive learning algorithm. (C) 2000 Elsevier Science Ltd. All rights reserved.
