Method for measuring energy saving on highly reflective coatings

 
Real energy savings data for residential buildings is scarce and difficult to find. To address this concern, two identical houses were built in the outskirts of Monterrey, Mexico, where summer temperatures are usually very high (~113°F). Energy consumptions using different waterproof coatings with different values of solar reflectance were evaluated. The results show that a white, highly reflective, waterproof coating reduces the cooling load by 12% against a conventional white waterproof coating. Furthermore, the highly reflective coating reduced the cooling load by 44% against a red waterproof coating and by 51% against a black asphalt roof coating. With the data generated an artificial neural network, of the feed forward back propagation type was trained using as inputs: solar radiation, temperature, humidity and wind speed. The output of this research was total energy consumption. The results generated by this model, will allow researchers to provide an estimate of potential energy savings in different weather conditions with high accuracy.