Occupancy Inference Through Energy Consumption Data: A Smart Home Experiment

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 i...

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Bibliographic Details
Published inComputer Vision Systems pp. 670 - 679
Main Authors Chouliara, Adamantia, Peppas, Konstantinos, Tsolakis, Apostolos C., Vafeiadis, Thanasis, Krinidis, Stelios, Tzovaras, Dimitrios
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Energy consumption
Machine learning
Occupancy inference
Random forest
Smart meters
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Summary: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%.
ISBN:3030349942
9783030349943
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-34995-0_61