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Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps

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  • Thiago Gomes Leal Ganhadeiro

    (Postgraduate Program in Computational Modeling in Science and Technology, Fluminense Federal University, Volta Redonda 27255-125, Brazil)

  • Eliane Da Silva Christo

    (Postgraduate Program in Computational Modeling in Science and Technology, Fluminense Federal University, Volta Redonda 27255-125, Brazil)

  • Lidia Angulo Meza

    (Postgraduate Program in Production Engineering, Fluminense Federal University, Niterói 24220-900, Brazil)

  • Kelly Alonso Costa

    (Postgraduate Program in Production Engineering, Fluminense Federal University, Volta Redonda 27255-125, Brazil)

  • Danilo Pinto Moreira de Souza

    (Postgraduate Program in Computational Modeling in Science and Technology, Fluminense Federal University, Volta Redonda 27255-125, Brazil)

Abstract

This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X -factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X -factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation.

Suggested Citation

  • Thiago Gomes Leal Ganhadeiro & Eliane Da Silva Christo & Lidia Angulo Meza & Kelly Alonso Costa & Danilo Pinto Moreira de Souza, 2018. "Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps," Energies, MDPI, vol. 11(10), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2677-:d:174254
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    References listed on IDEAS

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    Cited by:

    1. Dhan Lord B. Fortela & Matthew Crawford & Alyssa DeLattre & Spencer Kowalski & Mary Lissard & Ashton Fremin & Wayne Sharp & Emmanuel Revellame & Rafael Hernandez & Mark Zappi, 2020. "Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion," Clean Technol., MDPI, vol. 2(2), pages 1-14, April.
    2. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2020. "Open Data and Energy Analytics," Energies, MDPI, vol. 13(9), pages 1-3, May.
    3. Giulio Vialetto & Marco Noro, 2019. "Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator," Energies, MDPI, vol. 12(23), pages 1-16, November.
    4. Álvaro L. Ferreira & Tomás C. de Castro & Marcelo A. Costa & Sérgio H. R. Ribeiro & Iguatinan G. Monteiro, 2023. "Financial sustainability disparities among energy distribution companies: a multi-factor study case in Brazil," SN Business & Economics, Springer, vol. 3(7), pages 1-35, July.
    5. Hongwei Tang & Anping Lin & Wei Sun & Shuqi Shi, 2020. "An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments," Energies, MDPI, vol. 13(12), pages 1-18, June.

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