Data Fusion of Physical Variables as Bearing for User Thermal Comfort Analysis in Closed Environments

Context-aware systems use data obtained from variables, for example environmental, to adapt to changes in the environment. Due to this, it is necessary to analyze these variables to maintain healthy environmental levels and ensure users thermal-comfort in work spaces. In this sense, data fusion provides a way to unify data as a support for analysis of users thermal comfort. Thus, for this work, a sensor network was implemented to acquire data on environmental variables, such as temperature, humidity, air quality, light intensity, and noise level. While for the data fusion, Bayesian estimation was used as a method to generate knowledge about integrated values as a support for the analysis of users thermal comfort. This analysis was based on the specifications of the ASHRAE 55–2017 standard, which defines the thermal comfort zones according to users activities, and season. Results showed that the values of environmental variables were close to the thermal zone considered comfortable in summer for users. So, to improve thermal comfort conditions, other ventilation, cooling and air extraction systems could be installed in the work area. The importance of these analyses is to ensure a healthy and comfortable environment for users while performing daily activities.