Global Comfort Indices in Indoor Environments: A Survey
The term “comfort” has a number of nuances and meanings according to the specific context. This study was aimed at providing a review of the influence (or “weight”) of the different factors that contribute to global comfort, commonly known as indoor environmental quality (IEQ). A dedicated section i...
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/37df69169e9446c5983260cb7f2c1191 |
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| Sumario: | The term “comfort” has a number of nuances and meanings according to the specific context. This study was aimed at providing a review of the influence (or “weight”) of the different factors that contribute to global comfort, commonly known as indoor environmental quality (IEQ). A dedicated section includes the methodologies and strategies for finding the most relevant studies on this topic. Resulting in 85 studies, this review outlines 27 studies containing 26 different weightings and 9 global comfort indices (GCIs) with a formula. After an overview of the main concepts, basic definitions, indices, methods and possible strategies for each type of comfort, the studies on the IEQ categories weights to reach a global comfort index are reviewed. A particular interest was paid to research with a focus on green buildings and smart homes. The core section includes global indoor environmental quality indices, besides a specific emphasis on indices found in recent literature to understand the best aspects that they all share. For each of these overall indices, some specific details are shown, such as the comfort categories, the general formula, and the methods employed. The last section reports IEQ elements percentage weighting summary, common aspects of GCIs, requisites for an indoor global comfort index (IGCI), and models adopted in comfort category weighting. Furthermore, current trends are described in the concluding remarks pointing to a better IGCI by considering additional aspects and eventually adopting artificial intelligence algorithms. This leads to the optimal control of any actuator, maximising energy savings. |
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