Estimation of continuous-time models for the heat dynamics of a building

This paper describes a method for estimation of continuous-time models for the heat dynamics of buildings based on discrete-time building performance data. The parameters in the continuous-time model are estimated by the maximum likelihood method where a Kalman filter is used in calculating the likelihood function. The modeling procedure is illustrated by using measurements from an experiment where the heat input from electrical heaters is controlled by a pseudorandom binary signal. For the considered building a rather simple model containing two time constants is found adequate. Owing to the continuous-time formulation the parameters of the model are directly physically interpretable. The performance of the model for both forecasting and simulation is illustrated.