Prediction and analysis of heating energy demand for detached houses by computer vision

Exterior images of real estate contain a large number of visual clues which allow conclusions about the heating energy demand (HED) of a building. Up to now, HED has been determined by specially trained experts such as architects, civil engineers, etc. either on the basis of consumption data or estimated demand values. In this article, we present a novel approach to determine the HED of detached houses. Our suggested approach is based solely on the visual appearance and assumes that exterior images of a building contain a variety of information that allows inferences about the HED of a building. For this, we use the powerful techniques of image analysis and computer vision which are already successfully used in different domains like surveillance, image search, and robotics. The results show that our approach works well and in addition to the HED, the construction period of a building can also be determined. Our algorithm achieves a classification accuracy of 62% for HED and 57% for construction age epoch. (C) 2019 Published by Elsevier B.V.