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

Leonardo Brain García Fernández, Anna Diva Plasencia Lotufo and Carlos Roberto Minussi ()
Additional contact information
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

Energies, 2023, vol. 16, issue 10, 1-30

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.

Keywords: electrical load forecasting in disaggregated level; machine learning; adaptive resonance theory; neural networks; support vector machine; wavelet filters (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/10/4110/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/10/4110/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:10:p:4110-:d:1147788

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().