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

摘要: In the U.S., 40% of energy consumption is from buildings, approximately 48% which consumed by heating, ventilation, and air conditioning (HVAC) systems. Implementing demand driven HVAC operations a way to reduce related consumption, ultimately achieve sustainable building maintenance. This relies on availability occupancy information, determines peak/off-hour modes impacts cooling/heating loads research proposes an monitoring system that built combination non-intrusive sensors can detect indoor temperature, humidity, CO2 concentration, door status, light, sound motion. The effectiveness each sensor in estimation evaluated. data communicated wirelessly, processed real time using back-propagation (BP) artificial neural network (ANN) algorithm. Field tests are carried out lab space shared up 9 people for 15 consecutive days. test results report overall detection rate over 90%, indicates ability proposed monitor information multi-occupancy spaces support operations.