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Intelligent building automation systems can reduce the energy consumption ofheating, ventilation and air-conditioning (HVAC) units by sensing the comfortrequirements automatically and scheduling the HVAC operations dynamically.Traditional building automation systems rely on fairly inaccurate occupancysensors and basic predictive control using oversimplified building thermalresponse models, all of which prevent such systems from reaching their fullpotential. Such limitations can now be avoided due to the recent developmentsin embedded system technologies, which provide viable low-cost computingplatforms with powerful processors and sizeable memory storage in a smallfootprint. As a result, building automation systems can now efficiently executehighly-sophisticated computational tasks, such as real-time video processingand accurate thermal-response simulations. With this in mind, we designed andimplemented an occupancy-predictive HVAC control system in a low-cost yetpowerful embedded system (using Raspberry Pi 3) to demonstrate the followingkey features for building automation: (1) real-time occupancy recognition usingvideo-processing and machine-learning techniques, (2) dynamic analysis andprediction of occupancy patterns, and (3) model predictive control for HVACoperations guided by real-time building thermal response simulations (using anon-board EnergyPlus simulator). We deployed and evaluated our system forproviding automatic HVAC control in the large public indoor space of a mosque,thereby achieving significant energy savings.

Automatic HVAC Control with Real-time Occupancy Recognition and Simulation-guided Model Predictive Control in Low-cost Embedded System