A review of thermal comfort models and indicators for indoor environments

Diana Enescu

Renewable and Sustainable Energy Reviews, 2017, vol. 79, issue C, 1353-1379

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.

Keywords: Thermal comfort; Building; Comfort index; Indoor air temperature; Predicted mean vote; Predicted percentage dissatisfied; Control strategy; Neural network; Autoregressive model; Fuzzy inference system (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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DOI: 10.1016/j.rser.2017.05.175

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