Dynamic HVAC Operations Based on Occupancy Patterns With Real-Time Vision- Based System

2017-05-01T00:00:00Z (GMT) by Siliang Lu
An integrated heating, ventilation and air-conditioning (HVAC) system is one of the most important components to determining the energy consumption of the entire building. For commercial buildings, particularly office buildings and schools, the heating and cooling loads are largely dependent on the occupant behavioral patterns such as occupancy rates and their activities. Therefore, if HVAC systems can respond to dynamic occupancy profiles, there is a large potential to reduce energy consumption. However, currently, most of existing HVAC systems operate without the ability to adjust supply air rate accordingly in response to the dynamic profiles of occupants. Due to this inefficiency, much of the HVAC energy use is wasted, particularly when the conditioned spaces are unoccupied or under-occupied (less occupants than the intended design). The solution to this inefficiency is to control HVAC system based on dynamic occupant profiles. Motivated by this, the research provides a real-time vision-based occupant pattern recognition system for occupancy counting as well as activity level classification. The proposed vision-based system is integrated into the existing HVAC simulation model of a U.S. office building to investigate the level of energy savings as well as thermal comfort improvement compared to traditional existing HVAC control system. The research is divided into two parts. The first part is to use an open source library based on neural network for real-time occupant counting and background subtraction method for activity level classification with a common static RGB camera. The second part utilizes a DOE reference office building model with customized dynamic occupancy schedule, including the number of occupant schedule, activity schedule and clothing insulation schedule to identify the potential energy savings compared with conventional HVAC control system. The research results revealed that vision-based systems can detect occupants and classify activity level in real time with accuracy around 90% when there are not many occlusions. Additionally, the dynamic occupant schedules indeed can bring about energy savings. Details of vision-based system, methodology, simulation configurations and results will be presented in the paper as well as potential opportunities for use throughout multiple types of commercial buildings, specifically focused on office and educational institutes.