LAPSE:2023.15658
Published Article
LAPSE:2023.15658
Providing Convenient Indoor Thermal Comfort in Real-Time Based on Energy-Efficiency IoT Network
March 2, 2023
Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of indoor parameters, enabling the improvement of the indoor occupants’ thermal comfort. Numerical results show the efficiency of our prediction scheme, reaching an accuracy of 96%, as well as our genetic-based scheme in optimizing the indoor thermal parameters by 85%.
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Keywords
Energy Efficiency, Genetic Algorithm, indoor thermal comfort monitoring, IoT network, Machine Learning
Subject
Suggested Citation
Brik B, Esseghir M, Merghem-Boulahia L, Hentati A. Providing Convenient Indoor Thermal Comfort in Real-Time Based on Energy-Efficiency IoT Network. (2023). LAPSE:2023.15658
Author Affiliations
Brik B: DRIVE EA1859, University of Bourgogne Franche-Comté, 58000 Nevers, France [ORCID]
Esseghir M: Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10000 Troyes, France
Merghem-Boulahia L: Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10000 Troyes, France
Hentati A: Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10000 Troyes, France
Esseghir M: Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10000 Troyes, France
Merghem-Boulahia L: Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10000 Troyes, France
Hentati A: Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10000 Troyes, France
Journal Name
Energies
Volume
15
Issue
3
First Page
808
Year
2022
Publication Date
2022-01-23
Published Version
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15030808, Publication Type: Journal Article
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LAPSE:2023.15658
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doi:10.3390/en15030808
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[v1] (Original Submission)
Mar 2, 2023
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Mar 2, 2023
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