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Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy

Author

Listed:
  • Leonardo Brain García Fernández

    (Electrical Engineering Department, UNESP—São Paulo State University, Av. Brasil 56, Ilha Solteira 15385-000, SP, Brazil)

  • Anna Diva Plasencia Lotufo

    (Electrical Engineering Department, UNESP—São Paulo State University, Av. Brasil 56, Ilha Solteira 15385-000, SP, Brazil)

  • Carlos Roberto Minussi

    (Electrical Engineering Department, UNESP—São Paulo State University, Av. Brasil 56, Ilha Solteira 15385-000, SP, Brazil)

Abstract

In recent years, electrical systems have evolved, creating uncertainties in short-term economic dispatch programming due to demand fluctuations from self-generating companies. This paper proposes a flexible Machine Learning (ML) approach to address electrical load forecasting at various levels of disaggregation in the Peruvian Interconnected Electrical System (SEIN). The novelty of this approach includes utilizing meteorological data for training, employing an adaptable methodology with easily modifiable internal parameters, achieving low computational cost, and demonstrating high performance in terms of MAPE. The methodology combines modified Fuzzy ARTMAP Neural Network (FAMM) and hybrid Support Vector Machine FAMM (SVMFAMM) methods in a parallel process, using data decomposition through the Wavelet filter db20. Experimental results show that the proposed approach outperforms state-of-the-art models in predicting accuracy across different time intervals.

Suggested Citation

  • Leonardo Brain García Fernández & Anna Diva Plasencia Lotufo & Carlos Roberto Minussi, 2023. "Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy," Energies, MDPI, vol. 16(10), pages 1-30, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4110-:d:1147788
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    References listed on IDEAS

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