A review of thermal comfort models and indicators for indoor environments

A review of thermal comfort models and indicators for indoor environments This paper reviews the most used thermal comfort models and indicators with their variants, discussing their usage in control problems referring to energy management in indoor applications. The first part addresses the recent literature referring to the thermal comfort concepts, models of human thermal comfort, thermal comfort models and indicators, thermal comfort standards, control systems, optimisation methods, and practical assessments. Then, the ambient and personal parameters used to represent thermal comfort and thermal sensation are recalled. The following part reviews the definitions and usage of a number of thermal comfort indices, mainly related to the Predicted Mean Vote (PMV), the Actual Mean Vote (AMV), and the Predicted Percentage Dissatisfied (PPD), with their modifications and variants, indicating a number of applications to different situations in indoor environments. The last part reviews the thermal comfort models used to define control strategies in indoor applications, discussing the characteristics and parameters of models based on artificial neural networks, autoregressive variants, fuzzy control, and hybrid models combining different approaches. The characteristics of these models and their usage to predict the indoor air temperature and the PMV index are discussed with reference to the identification of the several inputs used in relevant literature contributions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Renewable and Sustainable Energy Reviews Elsevier

A review of thermal comfort models and indicators for indoor environments

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
1364-0321
D.O.I.
10.1016/j.rser.2017.05.175
Publisher site
See Article on Publisher Site

Abstract

This paper reviews the most used thermal comfort models and indicators with their variants, discussing their usage in control problems referring to energy management in indoor applications. The first part addresses the recent literature referring to the thermal comfort concepts, models of human thermal comfort, thermal comfort models and indicators, thermal comfort standards, control systems, optimisation methods, and practical assessments. Then, the ambient and personal parameters used to represent thermal comfort and thermal sensation are recalled. The following part reviews the definitions and usage of a number of thermal comfort indices, mainly related to the Predicted Mean Vote (PMV), the Actual Mean Vote (AMV), and the Predicted Percentage Dissatisfied (PPD), with their modifications and variants, indicating a number of applications to different situations in indoor environments. The last part reviews the thermal comfort models used to define control strategies in indoor applications, discussing the characteristics and parameters of models based on artificial neural networks, autoregressive variants, fuzzy control, and hybrid models combining different approaches. The characteristics of these models and their usage to predict the indoor air temperature and the PMV index are discussed with reference to the identification of the several inputs used in relevant literature contributions.

Journal

Renewable and Sustainable Energy ReviewsElsevier

Published: Nov 1, 2017

References

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