Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy
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 di...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/10/4110 |
| _version_ | 1797600279668981760 |
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| author | Leonardo Brain García Fernández Anna Diva Plasencia Lotufo Carlos Roberto Minussi |
| author_facet | Leonardo Brain García Fernández Anna Diva Plasencia Lotufo Carlos Roberto Minussi |
| author_sort | Leonardo Brain García Fernández |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:47:17Z |
| format | Article |
| id | doaj.art-f3f0a1bbdbb948a7a8fc543f1bf0a477 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:47:17Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f3f0a1bbdbb948a7a8fc543f1bf0a4772023-11-18T01:13:02ZengMDPI AGEnergies1996-10732023-05-011610411010.3390/en16104110Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP StrategyLeonardo Brain García Fernández0Anna Diva Plasencia Lotufo1Carlos Roberto Minussi2Electrical Engineering Department, UNESP—São Paulo State University, Av. Brasil 56, Ilha Solteira 15385-000, SP, BrazilElectrical Engineering Department, UNESP—São Paulo State University, Av. Brasil 56, Ilha Solteira 15385-000, SP, BrazilElectrical Engineering Department, UNESP—São Paulo State University, Av. Brasil 56, Ilha Solteira 15385-000, SP, BrazilIn 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.https://www.mdpi.com/1996-1073/16/10/4110electrical load forecasting in disaggregated levelmachine learningadaptive resonance theoryneural networkssupport vector machinewavelet filters |
| spellingShingle | Leonardo Brain García Fernández Anna Diva Plasencia Lotufo Carlos Roberto Minussi Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy Energies electrical load forecasting in disaggregated level machine learning adaptive resonance theory neural networks support vector machine wavelet filters |
| title | Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy |
| title_full | Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy |
| title_fullStr | Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy |
| title_full_unstemmed | Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy |
| title_short | Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy |
| title_sort | development of a short term electrical load forecasting in disaggregated levels using a hybrid modified fuzzy artmap strategy |
| topic | electrical load forecasting in disaggregated level machine learning adaptive resonance theory neural networks support vector machine wavelet filters |
| url | https://www.mdpi.com/1996-1073/16/10/4110 |
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