Application of AI-based Image Processing for Occupancy Monitoring in Building Energy Management

Smart Buildings enable significant savings in energy and CO2 emissions by model-predictive methods. The building users have a considerable influence on the energetic building management. On the one hand, they dictate the comfort parameters to be set. On the other hand, they generate internal thermal gains through their presence, affect humidity, consume oxygen and produce carbon dioxide. The more precisely the user behavior is known, the more precisely and resource-efficiently the room climate control can be adapted to this user behavior. In this paper, an intelligent vision-based sensor concept is proposed and tested that is capable to estimate occupancy and activity inside a building. The contribution initially concentrates on functional buildings, since here, compared to residential buildings, there is an even greater need for use-oriented room air conditioning, including savings potential.