Economical and Non-Invasive Residential Human Presence Sensing via Temperature Measurement

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption in United States, encompassing about 25% of total residential energy usage. A significant portion of energy is wasted by unnecessary operation, such as overheating/overcooling or operation without occupants. Wasteful behaviors will consume twice the amount of energy compared to energy conscious behaviors. Many market programmable thermostats exist to address this problem, however, difficulties in persistent programming of such products and lack of understanding of underlying physics prevent users from achieving tangible impact. Hence, fully autonomous energy control system is desirable to engage as many people into energy conscious behaviors as possible. Occupancy measurement is necessary components to enable fully autonomous control. Occupancy information can save energy by automatically turn off the HVAC system when the building is not occupied, or floats to a more energy-efficient setback temperature when the activity level is low. A number of existing sensor solutions available on the market include Passive Infrared (PIR), ultrasonic, Bluetooth/GPS, and CO₂ sensors, but these are either too expensive, not user-friendly, or limited in detection scope. These sensors are also incapable of detecting whether or not the occupant is an animal or a human. The work in this paper proposes an economical, reliable, non-invasive package to both detect human presence in a residence of a wide variety of geometries at the time and predict future occupancy pattern, by utilizing temperature sensors. To accomplish this, thermal sensors will be attached to both ends of door handles to collect the temperature data. This data will allow us to create a schedule to identify human activity leaving and exiting the space. At the same time, we will be collecting the skin temperature to determine the human activity level for better identification of the thermal comfort zone for occupants. The prediction model for occupancy pattern will be developed from previous data by using machine learning algorithm. For verification, experimental setup was built to verify our model by comparing actual human presence data from a house with the measured and predicted occupancy pattern from the temperature sensors. Future steps include implementing a data fusion scheme into the model to combine information from multiple types of sensors.