Internet of things and machine learning applied to the thermal comfort of internal environments

Abstract

The internet of things connects objects to the internet, enabling the dialogue between devices and users, providing new opportunities for applications, such as thermal comfort. In the research, adequate sensors were used to measure the heat index, the thermal discomfort index and the temperature and humidity index based on the temperature and relative humidity of a remote indoor environment. This research evaluated the level of thermal comfort in real-time using tools of storage, processing and analysis of big data information from the collection of IoT devices. With the analysis of the environment, it is possible to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms were also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment. Health researchers, for example, have the necessary knowledge to evaluate clinical data, but they are not used to using data analysis resources and machine learning algorithms. The platform was developed to reduce dependence on data experts and encourage healthcare researchers to develop their own models by automating the steps required for model development, using automated machine learning (AutoML).