Application of self-organizing maps for classification and filtering of electrical customer load patterns

  1. Valero, S. 4
  2. Ortiz, M. 4
  3. Senabre, C. 4
  4. Álvarez, C. 3
  5. García Franco, F.J. 3
  6. Encinas, N. 3
  7. Gabaldón, A. 1
  8. Fuentes, J.A. 1
  9. Ramírez-Rosado, I.J. 2
  10. Fernández-Jiménez, L.A. 2
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  3. 3 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

  4. 4 Universidad Miguel Hernández de Elche
    info

    Universidad Miguel Hernández de Elche

    Elche, España

    ROR https://ror.org/01azzms13

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Libro:
International Conference on Power nad Energy Systems

Editorial: 92

ISBN: 0-88986-326-1

Año de publicación: 2004

Páginas: 87-92

Tipo: Capítulo de Libro

SCOPUS: 2-s2.0-19944429376 GOOGLE SCHOLAR

Resumen

The objective of this paper is to show the capability of the Self-Organizing Maps (SOMs) to organize, to filter, to classify and to extract patterns from distributor, commercializer, aggregator or customer electrical demand databases -objective known as data mining-. This approach basically uses -to reach the above mentioned objectives- the historic load demand curves of each user. In our case, and for simplicity, we will study two typical medium users: an industry and a university located both in Spain. The results clearly show the suitability of SOM approach to improve data management and to find easily coherent clusters between electrical users.