Abstract
Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment.
Highlights
Thermal Comfort (TC) is defined as the psychophysical satisfaction of an individual immersed in a thermal environment [1]
The standard approach for TC assessment is essentially based on a thermal physic model that does not consider any other factors and the complex state of mind that could affect the TC perception
The comparison of all information acquired by survey highlights the differences between the individual perception of TC, Thermal Sensation Vote (TSV) and GCZa, and those defined by the standards, PMV and Graphic Comfort Zone Method (GCZM), respectively
Summary
Francesco Salamone 1, * ID , Lorenzo Belussi 1 ID , Cristian Currò 2 , Ludovico Danza 1 , Matteo Ghellere 1 , Giulia Guazzi 1 , Bruno Lenzi 2 , Valentino Megale 2 and Italo Meroni 1. Conference on Sensors and Applications, 15–30 November 2017; doi:10.3390/ecsa-4-04908
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