  Intelligent building automation systems can reduce the energy consumption of
heating, ventilation and air-conditioning (HVAC) units by sensing the comfort
requirements automatically and scheduling the HVAC operations dynamically.
Traditional building automation systems rely on fairly inaccurate occupancy
sensors and basic predictive control using oversimplified building thermal
response models, all of which prevent such systems from reaching their full
potential. Such limitations can now be avoided due to the recent developments
in embedded system technologies, which provide viable low-cost computing
platforms with powerful processors and sizeable memory storage in a small
footprint. As a result, building automation systems can now efficiently execute
highly-sophisticated computational tasks, such as real-time video processing
and accurate thermal-response simulations. With this in mind, we designed and
implemented an occupancy-predictive HVAC control system in a low-cost yet
powerful embedded system (using Raspberry Pi 3) to demonstrate the following
key features for building automation: (1) real-time occupancy recognition using
video-processing and machine-learning techniques, (2) dynamic analysis and
prediction of occupancy patterns, and (3) model predictive control for HVAC
operations guided by real-time building thermal response simulations (using an
on-board EnergyPlus simulator). We deployed and evaluated our system for
providing automatic HVAC control in the large public indoor space of a mosque,
thereby achieving significant energy savings.
