IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2959-d558498.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2959/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/10/2959/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krarti, Moncef & Aldubyan, Mohammad, 2021. "Mitigation analysis of water consumption for power generation and air conditioning of residential buildings: Case study of Saudi Arabia," Applied Energy, Elsevier, vol. 290(C).
    2. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    3. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    4. Qadeer Ali & Muhammad Jamaluddin Thaheem & Fahim Ullah & Samad M. E. Sepasgozar, 2020. "The Performance Gap in Energy-Efficient Office Buildings: How the Occupants Can Help?," Energies, MDPI, vol. 13(6), pages 1-27, March.
    5. Lu Jiang & Xingpeng Chen & Bing Xue, 2019. "Features, Driving Forces and Transition of the Household Energy Consumption in China: A Review," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    6. Bragolusi, Paolo & D'Alpaos, Chiara, 2022. "The valuation of buildings energy retrofitting: A multiple-criteria approach to reconcile cost-benefit trade-offs and energy savings," Applied Energy, Elsevier, vol. 310(C).
    7. Li, Han & Wang, Zhe & Hong, Tianzhen & Parker, Andrew & Neukomm, Monica, 2021. "Characterizing patterns and variability of building electric load profiles in time and frequency domains," Applied Energy, Elsevier, vol. 291(C).
    8. Mahmud, Arafat & Dhrubo, Ehsan Ahmed & Ahmed, S. Shahnawaz & Chowdhury, Abdul Hasib & Hossain, Md. Farhad & Rahman, Hamidur & Masood, Nahid-Al, 2022. "Energy conservation for existing cooling and lighting loads," Energy, Elsevier, vol. 255(C).
    9. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    10. Ghofrani, Ali & Zaidan, Esmat & Abulibdeh, Ammar, 2022. "Simulation and impact analysis of behavioral and socioeconomic dimensions of energy consumption," Energy, Elsevier, vol. 240(C).
    11. Alvaro Llaria & Jessye Dos Santos & Guillaume Terrasson & Zina Boussaada & Christophe Merlo & Octavian Curea, 2021. "Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management," Energies, MDPI, vol. 14(9), pages 1-37, May.
    12. Ma, Zheng & Knotzer, Armin & Billanes, Joy Dalmacio & Jørgensen, Bo Nørregaard, 2020. "A literature review of energy flexibility in district heating with a survey of the stakeholders’ participation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    13. Zhang, Chunbo & Hu, Mingming & Laclau, Benjamin & Garnesson, Thomas & Yang, Xining & Tukker, Arnold, 2021. "Energy-carbon-investment payback analysis of prefabricated envelope-cladding system for building energy renovation: Cases in Spain, the Netherlands, and Sweden," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    14. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    15. Sylwia Słupik & Joanna Kos-Łabędowicz & Joanna Trzęsiok, 2021. "How to Encourage Energy Savings Behaviours? The Most Effective Incentives from the Perspective of European Consumers," Energies, MDPI, vol. 14(23), pages 1-25, November.
    16. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    17. Moncef Krarti, 2019. "Evaluation of Energy Efficiency Potential for the Building Sector in the Arab Region," Energies, MDPI, vol. 12(22), pages 1-45, November.
    18. Jung, Wooyoung & Jazizadeh, Farrokh, 2019. "Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions," Applied Energy, Elsevier, vol. 239(C), pages 1471-1508.
    19. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "HVAC Energy Saving Strategies for Public Buildings Based on Heat Pumps and Demand Controlled Ventilation," Energies, MDPI, vol. 14(17), pages 1-20, September.
    20. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2959-:d:558498. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.