- Data from regular smart electricity meters allows for accurate detection of occupancy in residential buildings
Detecting when a household is occupied by its residents is fundamental to enable a number of home automation applications. Current systems for occupancy detection usually require the installation of dedicated sensors, like passive infrared sensors, magnetic reed switches or cameras. Using data from smart electricity meters that are currently being installed in millions of homes makes additional hardware obsolete, thus reduces cost and effort to set up such systems.
We collected electricity time-series data from five households over a period of eight months, together with ground truth data on occupancy. The data has been used to train several standard machine learning algorithms.
The results show that by using common classification methods, it is possible to achieve occupancy detection accuracies of more than 80% in practical settings – despite the fact that many electrical appliances are also activated when homeowners are away, such as fridges, washing machines, and heating systems.
- Beckel, Kleiminger, Staake, and Santini (2013) Occupancy Detection from Electricity Consumption Data. Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, Rome, Italy, 1-8.
This project has been funded in parts by Stadwerke Thun and IB Aarau.
Wilhelm Kleiminger, Christian Beckel, Thorsten Staake, Silvia Santini, Friedemann Mattern
Non-Intrusive Load Monitoring: Do NILM-Researchers Promise too much?
Non-Intrusive Load Monitoring: An Updated Version of Hart's Algorithm
Base Load Estimation with Smart Meter Data
Occupancy Detection with Smart Meter Data
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