Multiple ARMAX modeling scheme for forecasting air conditioning system performance

System identification is a procedure to characterize the dynamic behavior of a system, subsystem or individual component from measured data. This paper presents a study on the modeling and parameter identification of air conditioning processes by using the mathematical black box modeling technique, autoregressive moving average exogeneous (ARMAX) structure. A generic multiple input multiple output (MIMO) ARMAX structure of typical air conditioning systems is developed, whose parameters are identified by using the recursive extended least squares (RELS) method. The performance of the model is compared with that of a single input single output (SISO) ARMAX model. A significant component of the determination of an ARMAX model is the selection of an appropriate model order. Models of different orders and the effects of properties are evaluated. Site measurements from an air conditioning system in a building are used for the testing and validation of the models in the study.