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Artículo
Electricity clustering framework for automatic classification of customer loads
Autor/es | Biscarri Triviño, Félix
Monedero Goicoechea, Iñigo Luis García Delgado, Antonio Guerrero Alonso, Juan Ignacio León de Mora, Carlos |
Departamento | Universidad de Sevilla. Departamento de Tecnología Electrónica |
Fecha de publicación | 2017 |
Fecha de depósito | 2018-07-04 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | TEC2013-40767-R
IDI- 20150044 |
Cita | 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Electricity clustering.pdf | 2.913Mb | [PDF] | Ver/ | |