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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Aya Nabil Sayed & Faycal Bensaali & Yassine Himeur & Mahdi Houchati, 2023. "Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing System," Energies, MDPI, vol. 16(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2388-:d:1085725
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    References listed on IDEAS

    as
    1. Abolfazl Mohammadabadi & Samira Rahnama & Alireza Afshari, 2022. "Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    2. Shahira Assem Abdel-Razek & Hanaa Salem Marie & Ali Alshehri & Omar M. Elzeki, 2022. "Energy Efficiency through the Implementation of an AI Model to Predict Room Occupancy Based on Thermal Comfort Parameters," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    3. Shoaib Azizi & Ramtin Rabiee & Gireesh Nair & Thomas Olofsson, 2021. "Effects of Positioning of Multi-Sensor Devices on Occupancy and Indoor Environmental Monitoring in Single-Occupant Offices," Energies, MDPI, vol. 14(19), pages 1-23, October.
    4. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    Full references (including those not matched with items on IDEAS)

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