Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Luis Hernández, Carlos Baladrón, Javier M. Aguiar, Belén Carro and Antonio Sánchez-Esguevillas
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Luis Hernández: Centre for Energy, Environment and Technology Research (CIEMAT), Autovía de Navarra A15, Salida 56, 42290 Lubia, Soria, Spain
Carlos Baladrón: Department of Signal Theory, Communications and Telematics Engineering (E.T.S.I. Telecomunicación), University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Javier M. Aguiar: Department of Signal Theory, Communications and Telematics Engineering (E.T.S.I. Telecomunicación), University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Belén Carro: Department of Signal Theory, Communications and Telematics Engineering (E.T.S.I. Telecomunicación), University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Antonio Sánchez-Esguevillas: Department of Signal Theory, Communications and Telematics Engineering (E.T.S.I. Telecomunicación), University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain

Energies, 2012, vol. 5, issue 12, 1-14

Abstract: 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.

Keywords: industrial park; pattern recognition; self-organizing map; k-means; clustering; energy demand (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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