Comparison of adaptive neuro-fuzzy inference system (ANFIS) and machine learning algorithms for electricity production forecasting

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorInst Tecnol Aeronaut (ITA)-
Autor(es): dc.creatorRodriguez, Elen Y. A.-
Autor(es): dc.creatorGamboa, Alexander A. R.-
Autor(es): dc.creatorRodriguez, Elias C. A.-
Autor(es): dc.creatorSilva, Aneirson F. da-
Autor(es): dc.creatorRizol, Paloma M. S. R.-
Autor(es): dc.creatorMarins, Fernando A. S.-
Data de aceite: dc.date.accessioned2025-08-21T21:11:59Z-
Data de disponibilização: dc.date.available2025-08-21T21:11:59Z-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TLA.2022.9885166-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/237688-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/237688-
Descrição: dc.descriptionCombined cycle power plants (CCPP) are popular in the energy sector for the production of electricity, and are the union of two thermodynamic cycles, corresponding to the steam turbine and the gas turbine. This paper presents the application of several machine learning (ML) techniques and the adaptive neuro-fuzzy inference system (ANFIS) to predict the hourly electricity production in a CCPP. The models were developed using 5-fold cross-validation with the collected features of temperature, exhaust pressure, relative humidity, ambient pressure, and electricity production per hour (the target feature). The hyperparameters of the tested models were optimized. The correlation and determination coefficients of the models were higher than 92%, showing a significant performance. The ANFIS (r = 98% e R2 = 95%) model shows the lowest values in the evaluated error metrics, compared to the other ML models. Finally, the results showed the effectiveness of ANFIS in predicting the hourly production of electricity in CCPP.-
Descrição: dc.descriptionUniv Estadual Paulista, Fac Engn Guaratingueta, Guaratingueta, Brazil-
Descrição: dc.descriptionInst Tecnol Aeronaut, Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista, Fac Engn Guaratingueta, Guaratingueta, Brazil-
Formato: dc.format2288-2294-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-inst Electrical Electronics Engineers Inc-
Relação: dc.relationIeee Latin America Transactions-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectElectricity-
Palavras-chave: dc.subjectPower generation-
Palavras-chave: dc.subjectFuzzy neural networks-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectPredictive models-
Título: dc.titleComparison of adaptive neuro-fuzzy inference system (ANFIS) and machine learning algorithms for electricity production forecasting-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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