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Radiant floor heating systems have become a reference technology, but their use for cooling purposes has proven inconvenient in many applications due to their reduced cooling capacity and condensation issues. Nonetheless, potentialities and drawbacks of radiant floor cooling systems have been frequently addressed and simulated, given the large potential advantages of employing a single emissive system for all seasons. This paper aims to provide a comprehensive review of the modeling methods for radiant floor cooling systems proposed in scientific papers and also used in simulation software and technical standards. Models are classified according to their characterizing features, and the distinctive contributions of each method are discussed. Additionally, the modeling of the most relevant phenomena affecting floor performance is further discussed. The review revealed the presence of two main modeling classes, one only focusing on the floor's conductive heat transfer and the other integrating active floor analysis within the building's energy model of the thermal zone. Despite the presence of many modeling methods that are able to consider the most important effects in the radiant cooling system operation, not all the phenomena present in a practical application are fully described. Therefore, there is an ongoing need for more comprehensive, possibly easily characterizable, modeling approaches. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is changing the way individuals, worldwide, feel about staying in public indoor spaces. A strict control of indoor air quality and of people’s presence in buildings will be the new normal, to ensure a healthy and safe environment. Higher ventilation rates with fresh air are expected to be a requirement, especially in educational buildings, due to their high crowding index and social importance. Yet, in this framework, an increased use of primary energy may be overlooked. This paper offers a methodology to efficiently manage complex HVAC systems in educational buildings, concurrently considering the fundamental goals of occupants’ health and energy sustainability. The proposed fourstep procedure includes: dynamic simulation of the building, to generate synthetic energy loads; clustering of the energy data, to identify and predict typical building use profiles; day-ahead planning of energy dispatch, to optimize energy efficiency; dynamic adjustment of air changes, to guarantee a safe indoor air quality. Clustering and forecasting energy needs are expected to become particularly effective in a highly regulated context. The technique has been tested on two university classroom buildings, considering pre-lockdown attendance. This notwithstanding, quality and significance of the obtained thermal energy clusters push towards a benchmark post-pandemic application.