A Comfort-Based Approach to Smart Heating and Air Conditioning

A Review of Recent Literature on Systems and Methods for the Control of Thermal Comfort in Buildings

Thermal comfort in indoor environments is perceived as an important factor for the well-being and productivity of the occupants. To practically create a comfortable environment, a combination of models, systems, and procedures must be applied. This systematic review collects recent studies proposing complete thermal-comfort-based control strategies, extracted from a scientific database for the period 2017–2021. The study consists of this paper and of a spreadsheet recording all the 166 reviewed works. After a general introduction, the content of the papers is analyzed in terms of thermal comfort models, indoor environment control strategies, and correlation between these two aspects. Practical considerations on scope, required inputs, level of readiness, and, where available, estimated cost are also given. It was found that the predicted mean vote is the preferred thermal comfort modeling approach, followed by data-driven and adaptive methods. Thermal comfort is controlled mainly through indoor temperature, although a wide range of options are explored, including the comfort-based design of building elements. The most popular field of application of advanced control strategies is office/commercial buildings with air conditioning systems, which can be explained by budget and impact considerations. The analysis showed that few works envisaging practical implementations exist that address the needs of vulnerable people. A section is, therefore, dedicated to this issue.

Trustworthy human-AI partnerships

In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organizations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.