Abstract
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.
Highlights
The outbreak of COVID-19 pandemic has brought global attention to the need of a strict indoor air quality control, to prevent airborne virus transmission and avoid unsustainable lockdown of activities in crowded indoor spaces
Educational buildings have been shut in an early phase of the emergency, due to the epidemic risk associated with the high density of young, socially interacting, and often asymptomatic individuals
As a novel contribution to this aim, the present paper proposes a general procedure that is based on dynamic simulation of the building, clustering of energy demand data, and day-ahead planning of energy systems operation for minimization of non-renewable primary energy uses, followed by a variable air volume (VAV) ventilation, based on actual people’s presence, to keep concentration of pollutants and airborne microorganisms below risk thresholds
Summary
The outbreak of COVID-19 pandemic has brought global attention to the need of a strict indoor air quality control, to prevent airborne virus transmission and avoid unsustainable lockdown of activities in crowded indoor spaces. If on one hand it is not likely that people’s presence in indoor spaces will return to be loosely regulated as in the pre-pandemic era, on the other hand, there is plenty of room to optimize the energy management of HVAC systems and of the integrated energy-generation and storage devices, without compromising on the air quality levels required by health authorities In this respect, correctly forecasting the profiles of building energy needs, including ventilation, becomes paramount, so that daily energy generation and dispatch can be appropriately planned to dynamically meet those demands in the most energy-efficient and environmental-friendly way. The paper is structured as follows: clustering approaches applied to the energy needs of buildings will be reviewed, showing the usefulness of this dataprocessing technique; in Section 3, the proposed energy-management procedure will be illustrated, starting from the dynamic simulation of the building, up to the identification of the optimal energy-dispatch sequence in each cluster of forecast daily loads and the realtime control of air changes necessary to maintain a healthy indoor environment; in Section 4, dynamic simulation and data clustering will be applied to the thermal energy demand profiles of two classroom buildings of the University of Pisa, implementing actual pre-pandemic attendance, which is a more challenging problem than the one posed by future paradigms that involve a programmed presence and space distribution of students in classrooms
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