Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment.

The article describes the results of the project “open source smart lamp” aimed at designing and developing a smart object able to manage and control the indoor environmental quality (IEQ) of the built environment. A first version of this smart object, built following a do-it-yourself (DIY) approach using a microcontroller, an integrated temperature and relative humidity sensor, and techniques of additive manufacturing, allows the adjustment of the indoor thermal comfort quality (ICQ), by interacting directly with the air conditioner. As is well known, the IEQ is a holistic concept including indoor air quality (IAQ), indoor lighting quality (ILQ) and acoustic comfort, besides thermal comfort. The upgrade of the smart lamp bridges the gap of the first version of the device providing the possibility of interaction with the air exchange unit and lighting system in order to get an overview of the potential of a nearable device in the management of the IEQ. The upgraded version was tested in a real office equipped with mechanical ventilation and an air conditioning system. This office was occupied by four workers. The experiment is compared with a baseline scenario and the results show how the application of the nearable device effectively optimizes both IAQ and ILQ.

The Indoor Environmental Quality (IEQ) refers to the quality of the environment in relation to the health and well-being of the occupants. It is a holistic concept, which considers several categories, each related to a specific environmental parameter. This article describes a low-cost and open-source hardware architecture able to detect the indoor variables necessary for the IEQ calculation as an alternative to the traditional hardware used for this purpose. The system consists of some sensors and an Arduino board. One of the key strengths of Arduino is the possibility it affords of loading the script into the board’s memory and letting it run without interfacing with computers, thus granting complete independence, portability and accuracy. Recent works have demonstrated that the cost of scientific equipment can be reduced by applying open-source principles to their design using a combination of the Arduino platform and a 3D printer. The evolution of the 3D printer has provided a new means of open design capable of accelerating self-directed development. The proposed nano Environmental Monitoring System (nEMoS) instrument is shown to have good reliability and it provides the foundation for a more critical approach to the use of professional sensors as well as for conceiving new scenarios and potential applications.