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The note reports on recent developments to the coarse-grain method (CGM) of modeling transient heat flow in buildings. CGM was originally developed as an alternative to conventional fine-grain modeling techniques [such as the finite-difference method (FDM) and finite-element method (FEM)] to increase simulation speed to a degree that facilitates three-dimensional modeling, and to ease the tasks of model development and experimentation. Earlier work has shown that CGM can provide reasonably accurate simulations at a processing speed several orders of magnitude faster than FDM or FEM. This note describes and demonstrates refinements to the CGM approach that increase its modeling accuracy to a level comparable to FEM, while doubling its processing speed. These refinements are: (1) the use of a hybrid linear regression model with an artificial neural network (ANN) to represent each coarse-grain modeling element (the hybridization of the ANN effectively halves its complexity); and (2) a linear calibration of the ANN-based coarse-grain modeling elements to account for an observed positive bias in their predictions. The improved approach is demonstrated for a two-dimensional model of a bay in a research building located at the University of Florida. The note concludes with some suggestions for continuing research. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a new approach for simulating the thermal behavior of buildings that overcome the limitations of conventional heat-transfer simulation methods such as the finite difference method and the finite element method. 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 (1) simplified model construction for the end-user; (2) simplified model reconfiguration; (3) significantly faster simulation runs (orders of magnitude faster for two- and three-dimensional models); and (4) 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 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. [ABSTRACT FROM AUTHOR]