  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.
