towards smarter sensors: deep reinforcement learning for building automation
Name: Francesco Fraternali
Grad Year: 2019
On the last decades, an increasing number of office buildings around the world are equipped with sensor-based systems designed to reduce energy consumption while maintaining user-comfort. The majority of those systems are embedded with one or more between passive infrared sensors (PIRs), light or temperature sensors allowing to automatically adjust lights and temperature when someone enters or leaves a room. Furthermore, many of those sensors are battery powered and a periodical battery replacement is needed. However, this is just the beginning of the smart building revolution. A truly smart building will know how the office space is being used at every single moment: how many people are in each room, how long the line is in the dining room, where there is a free desk and many other aspects of the building usage. In order to achieve this goal, the next generation of sensors will need to be much smarter and able to source and analyze richer levels of data, enabling the execution of more sophisticated tasks including the management power management to avoid battery replacement.
Industry Application Area(s)
Control Systems | Energy/Clean technology | Software, Analytics