Detecting Presence From a WiFi Router’s Electric Power Consumption by Machine Learning

Presence and occupancy detection in residential and office environments is used to predict movement of people, detect intruders, and manage electric power consumption. Specifically, we are developing methods to improve demand side electrical power management by reducing electrical power waste in unoccupied spaces. In this paper, we conduct an extensive analysis on the applicability of using a WiFi router's electrical power consumption in different types of environments to determinate the number or people present in a space. We show the importance of a moving average filter for electrical load time series data, confirm the correlation between control packets and increased minimal router power consumption, and present our results on the accuracy of our approach. We conclude that a WiFi router's power consumption can improve presence detection in home environments and occupancy estimation in office environments, and where possible, should be analysed separately from the aggregated power consumption.