[PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (J.C.C. ); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (H.L.M.A. ;), [EMAIL_ADDRESS] (A.N.J.S. ), [EMAIL_ADDRESS] (A.v.F.H. ); [EMAIL_ADDRESS] (J.C.C. ); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (J.C.C. ); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (J.C.C. ); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (J.C.C. ); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; [EMAIL_ADDRESS] (J.C.C. ); [EMAIL_ADDRESS] (F.T.N.) [PERSON]  
1 School of Engineering, Sao Paulo State University (Unesp), Bauru 17033-360, SP, Brazil; agelema@gmail. Artificial neural networks have gained significant prominence in recent years, particularly in applications focused on data estimation, prediction, and classification across nearly all fields of knowledge. The growing availability of advanced technological tools, such as machine learning libraries and cloud computing platforms, has facilitated the development and implementation of these networks. Consequently, an increasing number of scientific articles have been published, demonstrating the effectiveness of neural networks in various applications [2; 3; 4; 5]. Reference [2] presents an artificial neural network for pattern recognition (PRN) applied to the classification of operational conditions in power transformers, distinguishing between normal state, thermal faults, and electrical faults based on the combustible gases present. The PRN demonstrated robust performance in both the training and validation phases (using samples not included in the training), achieving a mean squared error (MSE) close to the expected value (0.001). The neural network was able to classify the 815 presented samples with a 98% accuracy rate and achieved 100% accuracy in validation, highlighting that the developed methodology is an effective tool for diagnosing the operability of power transformers. In reference [3], an improved MGM(1,n) model was proposed. The method was used to forecast the short-term electricity consumption of Linfen City/China. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. Reference [4] introduces a new approach using ensembles of time series to forecast electricity demand in the Peruvian market one month in advance. This method processes the initial time series of electricity demand to address missing values, variance stabilization, normalization, stationarity, and seasonal issues. The results demonstrate that the proposed approach provides an efficient and accurate forecast of electricity demand in the Peruvian market one month in advance. In reference [5], the total active and reactive power losses in electrical power systems were estimated using artificial neural networks. The input variables for the network included the loading factor as well as the real and reactive power generated at the reference bus. During validation, which used 20% of the data not included in the training, the network proved effective, achieving a mean squared error of approximately \(10^{-3}\). These results indicate that the neural network was able to accurately predict the total power losses based on load conditions, producing estimates very close to the actual values.