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Open AccessArticle

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

Department Area of Electrical Engineering, School of Energy and Mining Engineering, Campus de Vegazana s/n, University of León, 24071 León, Spain
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Environments 2018, 5(12), 133; https://doi.org/10.3390/environments5120133
Received: 7 November 2018 / Revised: 26 November 2018 / Accepted: 3 December 2018 / Published: 6 December 2018
(This article belongs to the Special Issue Renewable Energy Systems and Sources)
Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators. View Full-Text
Keywords: building energy index; temporary variation; smart metering; clustering; data mining building energy index; temporary variation; smart metering; clustering; data mining
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De la Puente-Gil, Á.; González-Martínez, A.; Borge-Diez, D.; Blanes-Peiró, J.J.; De Simón-Martín, M. Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study. Environments 2018, 5, 133.

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