Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

• Published in: Energy and buildings 2017-11, Vol.154, p.141-156
• Main Authors: Aftab, Muhammad, Chen, Chien, Chau, Chi-Kin, Rahwan, Talal
• Format: Article
• Language: English
• Subjects: Air conditioners; Air conditioning; Automatic control; Automatic HVAC control; Automation; Building automation; Building management systems; Buildings; Computer applications; Computer simulation; Computing costs; Control systems; Embedded system; Embedded systems; Energy conservation; Energy consumption; HVAC; HVAC equipment; Image processing; Learning algorithms; Mathematical models; Model predictive control; Occupancy; Occupancy recognition; Predictive control; Processors; Real time; Studies; Thermal response; Thermal simulation; Ventilation; Video
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2017.07.077

•Provide occupancy recognition by real-time video processing with 80-90% accuracy.•Enable prediction of future occupancy patterns under a variety of dynamic usage patterns.•Demonstrate real-time HVAC control guided by an on-board EnergyPlus simulator.•Implemented all features in a low-cost Raspberry Pi system platform.•Achieve over 30% increase in energy saving by using our automatic HVAC control system. 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.