Parameter estimation for low-order models of complex buildings

The improvement of the building sector energy efficiency becomes crucially important to attain a balance in many sectors. Reduction of the energy consumption in buildings by using model predictive control strategies is recognized as one of the essential solutions to achieve considerable energy savings. Due to the nature of thermodynamic processes in buildings the underlying models are mostly nonlinear and of high order. In this work Constrained Unscented Kalman Filter is employed to obtain a linear low order model of a large public building applicable for the predictive control. Through the comparison of results with the data generated by highly accurate building simulation software IDA Indoor Climate and Energy (IDA-ICE), it has been shown that the first order linear model for each zone, with separated nonlinearities related to the solar radiation effects, is sufficient to capture the main dynamics of the observed building.