E3S Web Conf.
Volume 197, 202075th National ATI Congress – #7 Clean Energy for all (ATI 2020)
|Number of page(s)||14|
|Section||Management of Energy Supply and Demand. Smart Grid|
|Published online||22 October 2020|
Clustering of educational building load data for defining healthy and energy-efficient management solutions of integrated HVAC systems
Department of Energy, Systems, Territory and Constructions Engineering (DESTEC), University of Pisa, 56122 Pisa, Italy
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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