engleski

Smart monitoring technologies for personal thermal comfort: A review

Improving the efficiency of building energy systems is crucial to reach a goal related to the high – performance building, particularly in the space heating and air - cooling do-main, which are considered as leading energy consumption systems in buildings. Current trends in the smart building paradigm allowed efficient implementation of user-centric approach in personal thermal comfort modelling. The potential technological solutions in that sense can bring various benefits leading towards improvement of the building energy performance. Existing papers published in the Scopus and Web of Science database have been reviewed in order to investigate a state of the art related to experimental practices in the development and application of monitoring technologies for personal thermal comfort. Consequently, the work brings insights in some of the main research findings in the area focusing on (I) inputs that affect personal thermal comfort, (II) smart technologies and methods used for sensing and the detection of defined inputs and (III) insights into the approaches of collected data processing. Parameters considered in reviewed literature rely on environmental and personal variables. Based on the conducted review, it can be indi- cated that room air temperature, relative humidity, air velocity and mean radiant tempera-ture (MRT) are frequently used environmental parameters in thermal comfort modelling. Among user – related indicators in the considered literature, skin temperature (hands, face) and metabolic rate (MET) are found to be the most common, followed by clothing insulation (CLO) and heart rate data. Some additional parameters are also found to be influenced by thermal comfort of individual (skin conductance, brain activity, heat flux, sweat rate, aural temperature, body core temperature). Subjective response from users is still unavoidable and recorded through questionnaires. The results of review indicated that technological solutions for detection of thermal comfort parameters in data-driven models are mainly based on network of connected sensors, and can be divided into cam-era-based technologies, wearable devices and connected sensor systems. Finally, open questions and gaps that inquire further research were also detected and discussed in de-tail.

thermal comfort ; personalized comfort models ; energy efficiency ; energy savings ; smart buildings

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