Household occupancy monitoring using electricity meters

Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building au-tomation systems. An automatic heating system may, for ex-ample, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from elec-tric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occu-pancy information with an accuracy between 83 % and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy

Poster Abstract: Using Unlabeled Wi-Fi Scan Data to Discover Occupancy Patterns of Private Households

This paper introduces the homeset algorithm, a novel approach to estimate occupancy schedules of private households from sensor data. The algorithm relies on unlabeled Wi-Fi scans and anonymized GPS traces collected by the mobile phones of household occupants and is able to autonomously determine the reliability of the computed schedules. We validate our approach using a data set from the Nokia Lausanne Data Collection Campaign that contains mobile phone traces of 38 participants over more than a year. 1