Coarse-grain method for simulating the thermal behavior of buildings

Abstract

The paper demonstrates and evaluates a new method for simulating the thermal behavior of buildings that overcomes the limitations of conventional transient heat flow simulation techniques such as the Finite Difference Method (FDM) and the Finite Element Method (FEM). The proposed method uses a coarse-grain approach to model development whereby each element represents a complete building component such as a wall, internal space, or floor. The thermal behavior of each coarse-grain element is captured using empirical modeling techniques such as artificial neural networks (ANNs). The main advantages of the approach compared to conventional simulation methods are: (a) simplified model construction for the end-user; (b) simplified model reconfiguration; (c) significantly faster simulation runs (orders of magnitude faster for two and three-dimensional models); and (d) potentially more accurate results. The viability of the approach is demonstrated through a number of experiments with a model of a composite wall. The approach is shown to be able to sustain highly accurate long-term simulation runs, if the coarse-grain modeling elements are implemented as ANNs. In contrast, an implementation of the coarse-grain elements using a linear model is shown to function inaccurately and erratically. The paper concludes with an identification of on-going work and future areas for development of the technique.