Developments in hyper real-time simulation of transient heat-flow in buildings

Abstract

This paper reports on the latest results in the development of a new approach for simulating the thermal behavior of buildings that overcomes the limitations of conventional heat-transfer simulation methods such as FDM and FEM. The proposed technique 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 paper demonstrates the viability of the approach through a number of experiments with a model of a composite wall. The approach is shown to be able to sustain highly accurate longterm 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.