Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.

Italians were the first European citizens to experience the lockdown due to Sars-Cov-2 in March 2020. Most employees were forced to work from home. People suddenly had to share common living spaces with family members for longer periods of time and convert home spaces into workplaces. This inevitably had a subjective impact on the perception, satisfaction and preference of indoor environmental quality and work productivity. A web-based survey was designed and administered to Italian employees to determine how they perceived the indoor environmental quality of residential spaces when Working From Home (WFH) and to investigate the relationship between different aspects of users’ satisfaction. A total of 330 valid questionnaires were collected and analysed. The article reports the results of the analyses conducted using a descriptive approach and predictive models to quantify comfort in living spaces when WFH, focusing on respondents’ satisfaction. Most of them were satisfied with the indoor environmental conditions (89% as the sum of “very satisfied” and “satisfied” responses for thermal comfort, 74% for visual comfort, 68% for acoustic quality and 81% for indoor air quality), while the layout of the furniture negatively influenced the WFH experience: 45% of the participants expressed an unsatisfactory or neutral opinion. The results of the sentiment analysis confirmed this trend. Among the Indoor Environmental factors that affect productivity, visual comfort is the most relevant variable. As for the predictive approach using machine learning, the Support Vector Machine classifier performed best in predicting overall satisfaction.

Many bibliographical studies have highlighted the positive effects of green roofs as technological solutions both for new and renovated buildings. The one-year experimental monitoring campaign conducted has investigated, in detail, some aspects related to the surface temperature variation induced by the presence of different types of vegetation compared to traditional finishing systems for flat roofs and their impact from an energy and environmental point of view. The results obtained underlined how an appropriate vegetative solution selection can contribute to a significant reduction of the external surface temperatures (10 °C–20 °C for I > 500 W/m² and 0 °C–5 °C for I < 500 W/m², regardless of the season) compared to traditional flat roofs. During the winter season, the thermal gradients of the planted surface temperatures are close to zero compared to the floor, except under special improving conditions. This entails a significant reduction of the energy loads from summer air conditioning, and an almost conservative behavior with respect to that from winter heating consumption. The analysis of the inside growing medium temperatures returned a further interesting datum, too: the temperature gradient with respect to surface temperature (annual average 4 °C–9 °C) is a function of solar radiation and involves the insulating contribution of the soil.

While the Italian standard UNI 11235:2015 establishes minimum performance criteria, the thermal resistance of the growing medium of green roofs is not included in national regulations. Instead, thermal transmittance limits for roofs are obtained by referring to other stratigraphic layers. In the absence of specific national and international standards for laboratory calculations of the thermal performance of growing media for green roofs, a multi-year laboratory testing campaign was carried out on 8 samples which aimed to define the thermal resistance reference values of growing media as a function of density and water content. Thermal conductivity varies between 0.046–0.179 W/mK for dry samples as a function of density and between 0.046–0.47 W/mK as a function of moisture content. Defining a reference method, laboratory tests and restitution of the output in performance curves, was based on 108 tests carried out according to and deviating from the standard based on both guarded hot plate and heat flow meter methods. The significance of the results has prompted researchers and industrial partners to engage in further investment and ongoing tests in this area, targeting the definition of a standard laboratory method to be presented worldwide.