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

Energy used for building heating, ventilating and air conditioning contributes to a great share in thetotal energy consumption worldwide. Better understanding and management of energy distribution inthose processes is essential for the improvement of process quality and efficiency of energy use. This paper presents a data-based mechanistic modelling approach to model the dynamic indoortemperature distribution in an imperfectly mixed ventilated airspace based on energy input to thesystem. 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 the energy and temperature distribution 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 (R2 > 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 'gamma' and local temperature change rate coefficient by heat load h (C/J) 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. This approach will allow to energy in climate control.