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An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction

Author

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  • Alessandro Floris

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
    National Inter-University Consortium for Telecommunications, University of Cagliari, 09123 Cagliari, Italy)

  • Simone Porcu

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
    National Inter-University Consortium for Telecommunications, University of Cagliari, 09123 Cagliari, Italy)

  • Roberto Girau

    (Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy)

  • Luigi Atzori

    (Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
    National Inter-University Consortium for Telecommunications, University of Cagliari, 09123 Cagliari, Italy)

Abstract

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2959-:d:558498
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    References listed on IDEAS

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    1. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
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    Cited by:

    1. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    2. Karam M. Al-Obaidi & Mohataz Hossain & Nayef A. M. Alduais & Husam S. Al-Duais & Hossein Omrany & Amirhosein Ghaffarianhoseini, 2022. "A Review of Using IoT for Energy Efficient Buildings and Cities: A Built Environment Perspective," Energies, MDPI, vol. 15(16), pages 1-32, August.
    3. 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.
    4. Bing Xiao & Xuexiu Jia & Dong Yang & Lingwen Sun & Feng Shi & Qitong Wang & Yongfei Jia, 2022. "Research on Classification Method of Building Function Oriented to Urban Building Stock Management," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
    5. Ismail Aouichak & Sébastien Jacques & Sébastien Bissey & Cédric Reymond & Téo Besson & Jean-Charles Le Bunetel, 2022. "A Bidirectional Grid-Connected DC–AC Converter for Autonomous and Intelligent Electricity Storage in the Residential Sector," Energies, MDPI, vol. 15(3), pages 1-19, February.

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