A dynamic model for human thermal comfort for smart building applications

TitreA dynamic model for human thermal comfort for smart building applications
Publication TypeJournal Article
Year of Publication2020
AuthorsMerabet, GH, Essaaidi, M, Benhaddou, D
JournalProceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Mots-clésAir conditioning, Anthropometric parameters, Building applications, Commercial building, Dynamic models, Environmental factors, Heating, Human thermal comfort, Indoor environment, Intelligent buildings, Office buildings, Strong correlation, Thermal comfort, Thermal sensations, Ventilation, Wireless sensor networks

Thermal comfort is closely related to the evaluation of heating, ventilation, and air conditioning systems. It can be seen as the result of the perception of the occupants of a given environment, and it is the product of the interaction of a number of personal and environmental factors. Otherwise, comfort issues still do not play an important role in the daily operation of commercial buildings. However, in the workplace, local quality effects, in addition to the health, the productivity that has a significant impact on the performance of the activities. In this regard, researchers have conducted, for decades, investigations related to thermal comfort and indoor environments, which includes developing models and indices through experimentations to establish standards to evaluate comfort and factors and set-up parameters for heating, ventilation, and air conditioning systems. However, to our best knowledge, most of the research work reported in the literature deals only with parameters that are not dynamically tracked. This work aims to propose a prototype for comfort measuring through a wireless sensor network and then presenting a model for thermal comfort prediction. The developed model can be used to set up a heating, ventilation, and air conditioning system to meet the expected comfort level. In particular, the obtained results show that there is a strong correlation between users’ comfort and variables such as age, gender, and body mass index as a function of height and weight. © IMechE 2019.




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