Parameter estimation for dynamic HVAC models with limited sensor information

This paper presents an approach for identifying critical model parameters in a HVAC system using limited sensor information. Both static and dynamic nonlinear models are addressed here. Two numerical search algorithms, nonlinear least squares and simplex search, are used to estimate the parameters. The parameter estimation algorithm developed is validated on two different experimental systems, to confirm the practicality of this approach. Knowing the model parameters accurately can lead to a better model for control and fault detection applications.