Markov modelling of HVAC systems

Dynamic simulations have been successfully applied to the modelling of building heating, ventilating and air-conditioning (HVAC) plant operation. These simulations are generally driven using time-series data as input. Whilst time-series simulations are effective, they tend to be expensive in terms of computer execution time. A possible method for reducing simulation time is to develop a probabilistic picture of the model, by characterising the model as being in one of several states. By determining the probability for being in each model state, predictions of long-term values of quantities of interest can then be obtained using ensemble averages. This study aims to investigate the applicability of the Markov modelling method for the above stated purpose in the simulation of HVAC systems. In addition, the questions of the degree of accuracy which can be expected, and the amount of time-savings which are possible are investigated. The investigation has found that the Markov modelling technique can be successfully applied to simulations of HVAC systems, but that assumptions commonly made concerning the independence of driving variables may often not be appropriate. An alternative approach to implementing the Markov method, taking into Z): account dependencies between driving variables is suggested, but requires further development to be fully effective. The accuracy of results has been found to be related to the sizes of the partial derivatives of the calculated quantity with respect to each of the variables on which it depends, the sizes of the variables' ranges, and the number of states assigned to each variable in developing the probabilistic picture of the model's state. A deterministic error bound for results from Markov simulations is also developed, based on these findings.