An Adaptive Neural Network model for thermal characterization of building components

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Building materials are usually characterized in stationary or almost-stationary conditions and mono dimensional heat flow regime. The existing standards (such as ISO 9869 or EN ISO 6946, EN 12664, EN 12667, ISO 8302 etc), require experiments carried out in steady-state conditions, with a very fine control of the measuring parameters with the aim to apply a simple and reproducible procedure for the determination of thermal properties. However, the thermodynamic conditions that lead to a steady-state operating mode and mono dimensional flow are very difficult to obtain (in real conditions) or very expensive and time consuming (in climate chambers). In this paper the authors present the development of a method for thermal characterization of building components, inferring the steady-state conditions, when only measures in transient conditions are available. The method, based on an adaptive linear neural network (ALNN) model also could be have the potentialities to determine the thermal diffusivity from a significant transient behavior ad hoc imposed. The study targets multilayered walls homogeneous and the results are compared with the experimental data measured by a climate chamber that operate according to the standard EN 12667.




Baccoli, R., Di Pilla, L., Frattolillo, A., & Mastino, C. C. (2017). An Adaptive Neural Network model for thermal characterization of building components. In Energy Procedia (Vol. 140, pp. 374–385). Elsevier Ltd.

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