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.

Indoor environmental quality (IEQ) has a high-level of impact on one’s health and productivity. It is widely accepted that IEQ is composed of four categories: thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic comfort. The main physical parameters that primarily represent these comfort categories can be monitored using sensors. To this purpose, the article proposes a wireless indoor environmental quality logger. In the literature, global comfort indices are often assessed objectively (using sensors) or subjectively (through surveys). This study adopts an integrated approach that calculates a predicted indoor global comfort index (P-IGCI) using sensor data and estimates a real perceived indoor global comfort index (RP-IGCI) based on questionnaires. Among the 19 different tested algorithms, the stepwise multiple linear regression model minimized the distance between the two comfort indices. In the case study involving a university classroom setting—thermal comfort and indoor air quality were identified as the most relevant IEQ elements from a subjective point of view. The model also confirms this findings from an objective perspective since temperature and CO2merge as the measured physical parameters with the most impacts on overall comfort.