Application of IoT and Machine Learning techniques for the assessment of thermal comfort perception. (August 2018)

• Record Type: Journal Article
• Authors: Salamone, Francesco; Belussi, Lorenzo; Currò, Cristian; Danza, Ludovico; Ghellere, Matteo; Guazzi, Giulia; Lenzi, Bruno; Megale, Valentino; Meroni, Italo
• Is Part Of: Energy procedia. Volume 148(2018)
• Page Start: 798
• Page End: 805
• Publication Date: 2018-08
• Subjects: indoor thermal comfort -- wearable -- nearable -- IoT -- machine learning -- parametric models; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2018.08.130
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Thermal comfort is traditionally assessed by using the PMV index defined according to the EN ISO 7730:2005 where the user passively interacts with the surrounding environment considering a physic-based model built on a steady-state thermal energy balance equation. The thermal comfort satisfaction is a holistic concept comprising behavioral, physiological and psychological aspects. This article describes a workflow for the assessment of the thermal conditions of users through the analysis of their specific psychophysical conditions overcoming the limitation of the physic-based model in order to investigate and consider other possible relations between the subjective and objective variables.