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Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques

Author

Listed:
  • Muhammad S. Aliero

    (School of Information Technology, Monash University, Subang Jaya 47500, Malaysia)

  • Muhammad F. Pasha

    (School of Information Technology, Monash University, Subang Jaya 47500, Malaysia)

  • David T. Smith

    (Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA)

  • Imran Ghani

    (Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA)

  • Muhammad Asif

    (Architectural Engineering Department, School of Engineering and Built Environment, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Seung Ryul Jeong

    (Graduate School of Business IT, Kookmin University, Seoul 05029, Republic of Korea)

  • Moveh Samuel

    (Department of Aeronautical Engineering, Istanbul Gelisim University, 34310 Istanbul, Turkey)

Abstract

Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the datasets and data pre-processing. The study compiles a new comprehensive public set of training datasets for building occupancy profile prediction with over 40,000 records. To the best of our knowledge, it is the largest dataset to date, with the most realistic and challenging setting in building occupancy prediction. Furthermore, to the best of our knowledge, this is the first study that attained a robust occupancy count by considering a multimodal input to a single output regression model through the mining and mapping of feature importance, which has advantages over statistical approaches. The proposed solution is tested in a living room with a prototype system integrated with various sensors to obtain occupant-surrounding environmental datasets. The model’s prediction results indicate that the proposed solution can obtain data, and process and predict the occupants’ presence and their number with high accuracy values of 99.7% and 99.35%, respectively, using random forest.

Suggested Citation

  • Muhammad S. Aliero & Muhammad F. Pasha & David T. Smith & Imran Ghani & Muhammad Asif & Seung Ryul Jeong & Moveh Samuel, 2022. "Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques," Energies, MDPI, vol. 15(23), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9231-:d:994663
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    References listed on IDEAS

    as
    1. Muhammad Saidu Aliero & Muhammad Asif & Imran Ghani & Muhammad Fermi Pasha & Seung Ryul Jeong, 2022. "Systematic Review Analysis on Smart Building: Challenges and Opportunities," Sustainability, MDPI, vol. 14(5), pages 1-28, March.
    2. Alessandro Floris & Simone Porcu & Roberto Girau & Luigi Atzori, 2021. "An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction," Energies, MDPI, vol. 14(10), pages 1-17, May.
    3. Asif Iqbal & Farman Ullah & Hafeez Anwar & Ata Ur Rehman & Kiran Shah & Ayesha Baig & Sajid Ali & Sangjo Yoo & Kyung Sup Kwak, 2020. "Wearable Internet-of-Things platform for human activity recognition and health care," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    4. Naveen Shirur & Christian Birkner & Roman Henze & Thomas M. Deserno, 2021. "Tactile Occupant Detection Sensor for Automotive Airbag," Energies, MDPI, vol. 14(17), pages 1-16, August.
    5. Antonio Rosato & Francesco Guarino & Sergio Sibilio & Evgueniy Entchev & Massimiliano Masullo & Luigi Maffei, 2021. "Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment," Energies, MDPI, vol. 14(17), pages 1-41, August.
    6. Ruoqing Zhu & Donglin Zeng & Michael R. Kosorok, 2015. "Reinforcement Learning Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1770-1784, December.
    7. Vivian W. Y. Tam & Laura Almeida & Khoa Le, 2018. "Energy-Related Occupant Behaviour and Its Implications in Energy Use: A Chronological Review," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
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