Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing System

Aya Nabil Sayed, Faycal Bensaali (), Yassine Himeur and Mahdi Houchati
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Aya Nabil Sayed: Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
Faycal Bensaali: Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
Yassine Himeur: College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates
Mahdi Houchati: Iberdrola Innovation Middle East, Doha 210177, Qatar

Energies, 2023, vol. 16, issue 5, 1-14

Abstract: Building automation and the advancement of sustainability and safety in internal spaces benefit significantly from occupancy sensing. While particular traditional Machine Learning (ML) methods have succeeded at identifying occupancy patterns for specific datasets, achieving substantial performance in other datasets is still challenging. This paper proposes an occupancy detection method using non-intrusive ambient data and a Deep Learning (DL) model. An environmental sensing board was used to gather temperature, humidity, pressure, light level, motion, sound, and Carbon Dioxide (CO 2 ) data. The detection approach was deployed on an edge device to enable low-cost computing while increasing data security. The system was set up at a university office, which functioned as the primary case study testing location. We analyzed two Convolutional Neural Network (CNN) models to confirm the optimum alternative for edge deployment. A 2D-CNN technique was used for one day to identify occupancy in real-time. The model proved robust and reliable, with a 99.75% real-time prediction accuracy.

Keywords: edge computing; occupancy detection; environmental data; image transformation; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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