Abstract
This work is addressing the problem of occupancy detection in domestic environments, which is considered crucial in the aspect of increasing energy efficiency in buildings. In particular, in contrast with most previous researches, which obtained occupancy data through dedicated sensors, this study is investigating the possibility of using total consumption solely obtained from central smart meters installed in the examined buildings. In order to evaluate the feasibility of this simplified approach, the supervised machine learning classifier Random Forest was trained and tested on the experimental dataset. Repeated simulation tests show encouraging results achieving a high average performance with accuracy of 85%.
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The SIT4Energy project has received funding from the German Federal Ministry of Education and Research (BMBF) and the Greek General Secretariat for Research and Technology (GSRT) in the context of the GreekGerman Call for Proposals on Bilateral Research and Innovation Cooperation, 2016.
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Chouliara, A., Peppas, K., Tsolakis, A.C., Vafeiadis, T., Krinidis, S., Tzovaras, D. (2019). Occupancy Inference Through Energy Consumption Data: A Smart Home Experiment. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_61
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