Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Energies

Luis Hernández¹  Carlos Baladrón²  Javier M. Aguiar²  Belén Carro² 

[1] Centre for Energy, Environment and Technology Research (CIEMAT), Autovía de Navarra A15, Salida 56, 42290 Lubia, Soria, Spain; E-Mail:;Department of Signal Theory, Communications and Telematics Engineering (E.T.S.I. Telecomunicación), University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; E-Mails:

关键词: industrial park;    pattern recognition;    self-organizing map;    k-means;    clustering;    energy demand;   

DOI  :  10.3390/en5125215

来源: mdpi

【 摘 要 】

Understanding of energy consumption patterns is extremely important for optimization of resources and application of green trends. Traditionally, analyses were performed for large environments like regions and nations. However, with the advent of Smart Grids, the study of the behavior of smaller environments has become a necessity to allow a deeper micromanagement of the energy grid. This paper presents a data processing system to analyze energy consumption patterns in industrial parks, based on the cascade application of a Self-Organizing Map (SOM) and the clustering k-means algorithm. The system is validated with real load data from an industrial park in Spain. The validation results show that the system adequately finds different behavior patterns which are meaningful, and is capable of doing so without supervision, and without any prior knowledge about the data.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.