Data-based mechanistic modelling of indoor temperature distributions based on energy input

Abstract

Energy used for building heating, ventilating and air conditioning contributes to a great share in the total energy consumption worldwide. Better understanding and management of energy distribution in those processes is essential for the improvement of process quality and efficiency of energy use. This paper presents the data-based mechanistic modelling approach which has been developed to model the dynamic indoor temperature distribution in an imperfectly mixed ventilated airspace based on energy input to the system. The combination of classical heat balance differential equations and the data-based modelling techniques for continuous-time system has brought a robust dynamic model suitable for model-based controlling and yet providing a profound insight at the energy and temperature distributions in ventilated systems. The effect of changing heat input on the temperature distribution inside a ventilated structure was studied. Dynamic response of indoor temperature to varying energy input could be explained by a second order transfer function model with a high coefficient of determination ( R 2 > 0.99), a low Young Identification Criterion (YIC < −2.3) and a low model standard error (SE < 0.028 °C). The physically meaningful model parameters as local heat load fraction γ and the coefficient of local temperature change h (°C J −1) were revealed. This modelling approach is very useful for future design of model-based predictive controller for zonal control of indoor temperature by the direct adjustment of heat load into ventilated structures.