True power consumption labeling and mapping of the health system of the Castilla y León region in Spain by clustering techniques. (January 2019) - la Puente-Gil, Álvaro de; González-Martínez, Alberto; Borge-Diez, David; Martínez-Cabero, Miguel-Ángel; Simón-Martín, Miguel de
Abstract: The latest revisions in April 2018 of the 2010/31/UE and 2012/27/UE Directives on Energy Efficiency and Energy Savings respectively, point out the need of the development of smart energy indexes for buildings with the aim to (i) supervise the energy consumption on the building sector -that currently represents up to one third of the total final energy consumption- and (ii) lead the appropriate actions to transform the current buildings stock to nearly Zero Energy Buildings and Positive Energy Buildings. From public managed buildings, the Health System is the first energy consumer with great difference with other government administration sectors, such as Education or General Administration. Moreover, the energy bill has great impact on the sustainability of the public health care system. However, very few real data were available to characterize the energy demand on public buildings, which are usually the most intensive energy consumers, and efficiency indexes were usually obtained from simulation results. Nevertheless, thanks to the deployment of Smart Metering systems in the last years, it is possible to access to the true energy demand profiles of hundreds of these buildings. In this paper, with three years historical monthly electrical energy consumption data from the health system of the region of Castilla y León in Spain -including hospitals, outpatient facilities, clinics and other medical institutions- and the application of data mining techniques, an end-use electrical energy analysis was conducted to cluster the building housing according to the energy consumption into several energy use intensity clusters and, then, an average value and a Reference Building Energy Index for each cluster is proposed. Thus, a true energy labeling of these buildings based on their distance to the Reference Building Energy Index is done and presented in georeferenced maps.