Parameter estimation for grey-box models of building thermal behaviour

•Monte Carlo sampling of parameter space is used to gain cognitive insight into the expected behaviour of estimation algorithms based on numerical optimisation of the mean square error between simulation and measurement data.•Randomised initial guess for numerical optimisation is used to show dispersion of estimated parameters.•Reduction of degrees of freedom in the estimation is shown to significantly reduce dispersion of estimated parameters.•Model validation on independent data is used to show that calibrated models could be used in a model predictive control system. Good models for building thermal behaviour are an important part of developing building energy management systems that are capable of reducing energy consumption for space heating through model predictive control. A popular approach to modelling the temperature variations of buildings is grey-box models based on lumped parameter thermal networks. By creating simplified models and calibrating their parameters from measurement data, the resulting model is both accurate and shows good generalisation capabilities. Often, parameters of such models are assumed to be a combination of different physical attributes of the building, hence they have some physical interpretation. In this paper, we investigate the dispersion of parameter estimates by use of randomisation. We show that there is significant dispersion in the parameter estimates when using randomised initial conditions for a numerical optimisation algorithm. Further, we claim that in order to assign a physical interpretation to grey-box model parameters, we require the estimated parameters to converge independently of the initial conditions and different datasets. Despite the dispersion of estimated parameters, the prediction capability of calibrated grey-box models is demonstrated by validating the models on independent data. This shows that the models are usable in a model predictive control system.