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Visual Data Mining: Using Self-Organizing Maps for Electricity Distribution Regulation

Hongyan Liu, Tomas Eklund, Barbro Back, Hannu Vanharanta, Visual Data Mining: Using Self-Organizing Maps for Electricity Distribution Regulation . In: Ezendu Ariwa, Eyas El-Qawasmeh (Eds.), Digital Enterprise and Information Systems, Communications in Computer and Information Science 194, 631–646, Sprigner-Verlag Berlin/Heidelberg, 2011.

http://dx.doi.org/10.1007/978-3-642-22603-8_55

Abstract:

The electricity distribution regulation and efficiency benchmarking practice in Finland has drawn attention because of its controversial regulatory scheme and arguably efficient electricity distribution sector. This study uses a computational intelligence tool, i.e., Self-Organizing Map (SOM), in the context of electricity distribution efficiency performance visualization. A SOM-model has been built based on collected data for 2001-2004. It allows the reader to discriminate between the Finnish DSOs’ differing operating circumstances. Through clustering and visualization, an overall perspective of the efficiency performance of the DSOs in 2001-2004 is rendered. In addition, such a visualization approach connects the DSO’s efficiency performance to its respective operating characteristics, which is otherwise not straightforwardly indicated by only studying efficiency scores. This application provides evidence that visual data mining with the SOM as a complementary approach in electricity distribution regulation and efficiency benchmarking has the potential to be expended to other regulatory practices (e.g., yardstick regulation).

BibTeX entry:

@INPROCEEDINGS{inpLiEkBaVa11a,
  title = {Visual Data Mining: Using Self-Organizing Maps for  Electricity Distribution Regulation },
  booktitle = {Digital  Enterprise and Information Systems},
  author = {Liu, Hongyan and Eklund, Tomas and Back, Barbro and Vanharanta, Hannu},
  volume = {194},
  series = {Communications in Computer and Information Science},
  editor = {Ariwa, Ezendu and El-Qawasmeh, Eyas},
  publisher = {Sprigner-Verlag Berlin/Heidelberg},
  pages = {631–646},
  year = {2011},
  keywords = {computational intelligence, visual data mining, artificial neural networks (ANNs), self-organizing maps (SOM), clustering, visualization, electricity distribution regulation, efficiency benchmarking, performance analysis},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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