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

• Autor(a) principal: Rodriguez, Elen Y. A. [UNESP]
• Data de Publicação: 2022
• Outros Autores: Gamboa, Alexander A. R., Rodriguez, Elias C. A. [UNESP], Silva, Aneirson F. da [UNESP], Rizol, Paloma M. S. R. [UNESP], Marins, Fernando A. S. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/TLA.2022.9885166; http://hdl.handle.net/11449/237688
• topic: Electricity; Power generation; Fuzzy neural networks; Machine learning; Predictive models

Combined 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.