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Article: Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality

TitleMeasuring the right factors: A review of variables and models for thermal comfort and indoor air quality
Authors
KeywordsBuilding control
Energy efficiency
Health
Indoor air quality
Neural network
Reinforcement learning
Thermal comfort
Ventilation system
Issue Date2021
Citation
Renewable and Sustainable Energy Reviews, 2021, v. 135, article no. 110436 How to Cite?
AbstractThe indoor environment directly affects health and comfort as humans spend most of the day indoors. However, improperly controlled ventilation systems can expend unnecessary energy and increase health risks, while improved thermal and air quality can often result in higher energy consumption. One way to approach this dilemma is by understanding the effectiveness of the variables influencing indoor air quality (IAQ) related health and comfort. The objective of this paper is to highlight evidence and variables from empirical and deterministic models, which are combined in analytical models that current machine learning techniques often overlook. This paper reviews the analytical models and identifies the corresponding input variables, discussing their application in models based on artificial neural networks (ANNs) and reinforcement learning (RL). ANN and RL models have accurately described non-linear systems with uncertain dynamics and provided predictive and adaptive control strategies. The first part of this study focuses on the most common thermal comfort models and their variables, mainly related to steady-state and adaptive models. The second part reviews typical models of determining indoor air pollutants and their relationship with ventilation requirements and health effects. Forty-five works closely related to the field are summarized in multiple tables. The last part identifies the factors needed to predict thermal comfort and IAQ.
Persistent Identifierhttp://hdl.handle.net/10722/334691
ISSN
2023 Impact Factor: 16.3
2023 SCImago Journal Rankings: 3.596
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, Nan-
dc.contributor.authorAviv, Dorit-
dc.contributor.authorGuo, Hongshan-
dc.contributor.authorBraham, William W.-
dc.date.accessioned2023-10-20T06:49:58Z-
dc.date.available2023-10-20T06:49:58Z-
dc.date.issued2021-
dc.identifier.citationRenewable and Sustainable Energy Reviews, 2021, v. 135, article no. 110436-
dc.identifier.issn1364-0321-
dc.identifier.urihttp://hdl.handle.net/10722/334691-
dc.description.abstractThe indoor environment directly affects health and comfort as humans spend most of the day indoors. However, improperly controlled ventilation systems can expend unnecessary energy and increase health risks, while improved thermal and air quality can often result in higher energy consumption. One way to approach this dilemma is by understanding the effectiveness of the variables influencing indoor air quality (IAQ) related health and comfort. The objective of this paper is to highlight evidence and variables from empirical and deterministic models, which are combined in analytical models that current machine learning techniques often overlook. This paper reviews the analytical models and identifies the corresponding input variables, discussing their application in models based on artificial neural networks (ANNs) and reinforcement learning (RL). ANN and RL models have accurately described non-linear systems with uncertain dynamics and provided predictive and adaptive control strategies. The first part of this study focuses on the most common thermal comfort models and their variables, mainly related to steady-state and adaptive models. The second part reviews typical models of determining indoor air pollutants and their relationship with ventilation requirements and health effects. Forty-five works closely related to the field are summarized in multiple tables. The last part identifies the factors needed to predict thermal comfort and IAQ.-
dc.languageeng-
dc.relation.ispartofRenewable and Sustainable Energy Reviews-
dc.subjectBuilding control-
dc.subjectEnergy efficiency-
dc.subjectHealth-
dc.subjectIndoor air quality-
dc.subjectNeural network-
dc.subjectReinforcement learning-
dc.subjectThermal comfort-
dc.subjectVentilation system-
dc.titleMeasuring the right factors: A review of variables and models for thermal comfort and indoor air quality-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rser.2020.110436-
dc.identifier.scopuseid_2-s2.0-85092170204-
dc.identifier.volume135-
dc.identifier.spagearticle no. 110436-
dc.identifier.epagearticle no. 110436-
dc.identifier.eissn1879-0690-
dc.identifier.isiWOS:000592374200003-

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