Energy Research Journal

Comparison of Green Roof Model Predictions with Experimental Data

Sergio Quezada-García, Manuela Azucena Escobedo-Izquierdo, Juan José Ambriz-García, Rodolfo Vázquez-Rodriguez and Diego Morales-Ramírez

DOI : 10.3844/erjsp.2015.15.24

Energy Research Journal

Volume 6, Issue 1

Pages 15-24

Abstract

In this study, the results of a green roof model are presented and compared with experimental data. Good agreement between simulation and experiment were found for different experimental conditions that were carried out in a controlled environment laboratory. The study of green roof heat transfer processes is important because it allows a better thermal design in buildings; the use of urban forest area reduces the urban heat island effect and others improvements such as air purification. Also, with a mathematical model that accurately describes the heat transfer in the green roof it is possible to determine variables such as the local and averaged temperature inside the building and thus obtain the energy savings of a green roof over a conventional one. In this study a mathematical model of heat transfer in a green roof considers that porous materials form some of its layers and that the heat sources are introduced into the model through boundary conditions. In order to validate the mathematical model considering porous materials, the data obtained from the model and laboratory experimental data were compared. The results show that the inclusion of equations considering porous materials, inside the heat transfer model of the green roof, described properly heat transfer processes. Considering porous materials in the green roof model allows including effects due to canopy density, the amount of water contained in the soil layer or penetration of the radiation through the green layer.

Copyright

© 2015 Sergio Quezada-García, Manuela Azucena Escobedo-Izquierdo, Juan José Ambriz-García, Rodolfo Vázquez-Rodriguez and Diego Morales-Ramírez. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.