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Applying IoT and Data Analytics to Thermal Comfort: A Review

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Book cover Machine Intelligence and Data Analytics for Sustainable Future Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 971))

Abstract

The widespread popularity of Internet of things (IoT) enables a huge amount of fine-grained thermal comfort and, implicitly, energy efficiency data to be assembled. Meanwhile, the movement toward a human-centric approach in the domain of smart homes has constantly been advancing worldwide. How to collect, prepare, store and employ the huge amount of data collected by the IoT devices to promote and enhance the thermal comfort inside buildings while preserving energy constraints is an important issue. To date, different IoT applications have been introduced in the thermal comfort domain. To provide a thorough overview of the prevailing research and to identify challenges for the upcoming research, this paper conducts a review on the IoT thermal comfort applications and thermal comfort analytics in buildings. Following the three stages of analytics: descriptive, predictive and prescriptive, we propose the basic application areas as thermal comfort analysis, thermal comfort prediction and thermal environment control. We also review the novel techniques endorsed by each application. In addition, we discuss some research trends associated with the topic like machine learning techniques, data privacy and security and federated learning.

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Acknowledgements

This work is partly supported by grants from Troyes Champagne métropole and the Conseil Départemental de l’Aube.

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Authors and Affiliations

  1. ICD/ERA, University of Technology of Troyes, 12 Rue Marie Curie, 10300, Troyes, France

    Maysaa Khalil, Moez Esseghir & Leila Merghem-Boulahia

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  1. Maysaa Khalil
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  2. Moez Esseghir
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  3. Leila Merghem-Boulahia
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Correspondence to Moez Esseghir .

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Editors and Affiliations

  1. Department of EECS, Vanderbilt University, Nashville, TN, USA

    Uttam Ghosh

  2. ENSAK, Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Yassine Maleh

  3. Charles Darwin University, Darwin, NT, Australia

    Mamoun Alazab

  4. Department of Computer Science and Engineering, Independent University, Dhaka, Bangladesh

    Al-Sakib Khan Pathan

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Khalil, M., Esseghir, M., Merghem-Boulahia, L. (2021). Applying IoT and Data Analytics to Thermal Comfort: A Review. In: Ghosh, U., Maleh, Y., Alazab, M., Pathan, AS.K. (eds) Machine Intelligence and Data Analytics for Sustainable Future Smart Cities. Studies in Computational Intelligence, vol 971. Springer, Cham. https://doi.org/10.1007/978-3-030-72065-0_10

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