A Non-Intrusive Occupancy Monitoring System for Demand Driven HVAC Operations

摘要

In the U.S., 40% of the energy consumption is from buildings, approximately 48% of which is consumed by heating, ventilation, and air conditioning (HVAC) systems. Implementing demand driven HVAC operations is a way to reduce HVAC related energy consumption, and ultimately to achieve sustainable building operations and maintenance. This relies on the availability of occupancy information, which determines peak/off-hour modes and impacts cooling/heating loads of HVAC systems. This research proposes an occupancy monitoring system that is built on a combination of non-intrusive sensors that can detect indoor temperature, humidity, CO_2 concentration, door status, light, sound and motion. The effectiveness of each sensor in occupancy estimation is evaluated. The sensor data is communicated wirelessly, and processed in real time using a back-propagation (BP) artificial neural network (ANN) algorithm. Field tests are carried out in a lab space that is shared by up to 9 people for 15 consecutive days. The test results report an overall detection rate of over 90%, which indicates the ability of the proposed system to monitor the occupancy information of multi-occupancy spaces in real time in support of demand driven HVAC operations.