Energy Efficiency Assessment for Buildings Based on the Generative Adversarial Network Structure

Thermal images are highly dependent on outside environmental conditions. This paper proposes a method for improving the accuracy of the measured outside temperature on buildings with different surrounding parameters, such as air humidity, external temperature, and distance to the object. A model was proposed for improving thermal image quality based on KMeans and the modified generative adversarial network (GAN) structure. It uses a set of images collected for objects exposed to different outside conditions in terms of the required weather recommendations for the measurements. This method improves the diagnosis of thermal deficiencies in buildings. Its results point to the probability that areas of heat loss match multiple infrared measurements with inconsistent contrast for the same object. The model shows that comparable accuracy and higher matching were reached. This model enables effective and accurate infrared image analysis for buildings where repeated survey output shows large discrepancies in measured surface temperatures due to material properties.