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Electricity clustering framework for automatic classification of customer loads

 

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Acceso restringido Electricity clustering framework for automatic classification of customer loads
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Author: Biscarri Triviño, Félix
Monedero Goicoechea, Iñigo Luis
García Delgado, Antonio
Guerrero Alonso, Juan Ignacio
León de Mora, Carlos
Department: Universidad de Sevilla. Departamento de Tecnología Electrónica
Date: 2017
Published in: Expert Systems with Applications, 86 (November 2017), 54-63.
Document type: Article
Abstract: Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.
Cite: Biscarri Triviño, F., Monedero Goicoechea, I.L., García Delgado, A., Guerrero Alonso, J.I. y León de Mora, C. (2017). Electricity clustering framework for automatic classification of customer loads. Expert Systems with Applications, 86 (November 2017), 54-63.
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URI: https://hdl.handle.net/11441/76656

DOI: 10.1016/j.eswa.2017.05.049

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