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Sensors 2018, 18(5), 1602; https://doi.org/10.3390/s18051602

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study

1
ITC-CNR, Construction Technologies Institute-National Research Council of Italy, Lombardia St., 49-20098 San Giuliano M.se, Italy
2
SCS, SoftCare Studios Srls, Franco Sacchetti St., 52-00137 Roma, Italy
This paper is an expanded version of our published paper: Salamone, F.; Belussi, L.; Danza, L.; Meroni, I. An integrated framework for users’ well-being. In Proceedings of the 4th International Electronic Conference on Sensors and Applications, 15–30 November 2017; doi:10.3390/ecsa-4-04908.
*
Author to whom correspondence should be addressed.
Received: 12 April 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
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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. View Full-Text
Keywords: indoor thermal comfort; wearable; nearable; IoT; machine learning; parametric models indoor thermal comfort; wearable; nearable; IoT; machine learning; parametric models
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Salamone, F.; Belussi, L.; Currò, C.; Danza, L.; Ghellere, M.; Guazzi, G.; Lenzi, B.; Megale, V.; Meroni, I. Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study . Sensors 2018, 18, 1602.

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