Occupancy Detection in Smart Housing Using Both Aggregated and Appliance-Specific Power Consumption Data

Abstract

Occupancy detection is a process of inferring the presence of persons in a space but without necessarily knowing their identities or quantity. Knowledge of occupancy is essential in building information modeling, building energy demand forecast, energy simulations, security, and smart control. Unlike other approaches that require the installation of additional sensors, such as cameras, infrared sensors, optical devices, sound sensors, within the targeted areas, we propose to improve a state of the art approach by Kleiminger, Beckel, and Santini that infers occupancy based on the aggregated power consumption data readily available in many residential houses. Our new approach integrates both aggregated and appliance specific electricity consumption data for occupancy detection. Evaluation of our new approach using the publicly available ECO dataset shows that our best approach can achieve an overall accuracy between 84% and 93%, which improves the baseline accuracy by 1% to 8%. However, our second experiment with a dataset that does not include data about HVAC yields much poorer performance than the first. Our central conclusion is that while appliances are usually strong predictors of occupancy, individual appliances rarely offer adequate coverage of a whole day as they tend to be used rather sporadically. They work well when combined with HVAC, as in the first experiment, but not so well when there is no HVAC, as in the second experiment. Therefore, the key for this approach to work well is when there are sufficient coverage from appliances, or perhaps to complement other data sources.