An improved lumped parameter method for building thermal modelling

Abstract

In this work an improved method for the simplified modelling of the thermal response of building elements has been developed based on a 5-parameter second-order lumped parameter model. Previous methods generate the parameters of these models either analytically or by using single objective function optimisation with respect to a reference model. The analytical methods can be complex and inflexible and the single objective function method lacks generality. In this work, a multiple objective function optimisation method is used with a reference model. Error functions are defined at both internal and external surfaces of the construction element whose model is to be fitted and the resistance and capacitance distributions are adjusted until the error functions reach a minimum. Parametric results for a wide range (45) of construction element types have been presented. Tests have been carried out using a range of both random and periodic excitations in weather and internal heat flux variables resulting in a comparison between the simplified model and the reference model. Results show that the simplified model provides an excellent approximation to the reference model whilst also providing a reduction in computational cost of at least 30%.

Computational modelling of thermal consumption of buildings with controlled interior temperature

“…All calculations are based on the one-dimensional model simplification, coming from the linearized heat conduction (no air flow is allowed) and on the application of the finite element method with linear splines as basis functions, with the original software implementation inside the MATLAB environment. Thus it is useful to compare our model with the approach of [22], based on the analogy with the analysis of electrical RC-circuits; a relevant model circuit is described in [15], p. 447, and [22], p. 156, in all details. The "lumped masses" by [22] generate diagonal matrices M (in our notation); here we come to (slightly more general) sparse matrices M, with technical details explained in [13], p. 62.…”

“…Thus it is useful to compare our model with the approach of [22], based on the analogy with the analysis of electrical RC-circuits; a relevant model circuit is described in [15], p. 447, and [22], p. 156, in all details. The "lumped masses" by [22] generate diagonal matrices M (in our notation); here we come to (slightly more general) sparse matrices M, with technical details explained in [13], p. 62.…”

“…The progress in this research area in the last 2 decades can be documented on [19], [15] and [22] and on numerous further references therein. Strong physical and geometrical simplifications are often overcome using some elements of soft computing by [17]: fuzzy sets, rough classifications, genetic algorithms, etc.…”

A Novel Modelling Approach for Condensing Boilers Based on Hybrid Dynamical Systems

“…Therefore, if the extended surface temperature gradient is accounted for by an effectiveness term, heat transfer from combustion gas will be with reference to extended surface temperature and the heat transfer from water will be with respect to tube wall surface temperature. The evolution of combustion gas temperature T a in ( • C) is given by [24]:…”

A Dynamic Model for Indoor Temperature Prediction in Buildings

“…On the other hand, data driven models [18][19][20][21][22][23][24][25][26][27][28] are based solely on measurements and are typically identified without information on the physical nature of the building properties. Hybrid or grey box models are a combination of data driven and physical modelling approaches [29][30][31][32][33][34][35]. This model type needs sufficient amount of measurement data for parameter identification and some information about the properties of the building.…”