## 4 Conclusions  
This work presented a comprehensive analysis of the application of artificial neural networks (ANN) in forecasting electrical energy consumption. The study involved training and validating an ANN with 15 neurons in the hidden layer, adjusting weights and biases to achieve high accuracy in predictions. The results demonstrated that the ANN was able to accurately predict energy consumption values for a residence, surpassing polynomial regression techniques. The study utilized 1484 samples for training and validation and an additional 144 samples for future forecasting, incorporating input variables such as voltage, current, active power, power factor, frequency, and time (days and minutes). The analysis shows that the ANN excelled in modeling energy consumption patterns, delivering results very close to the desired values and proving its effectiveness as a robust tool for future predictions. The ANN's ability to model complex, nonlinear energy consumption patterns was validated by its performance across key metrics, such as MAE (0.21532) and MAPE (1.8356%), which indicate a high level of accuracy, confirming that the predictions were consistently close to actual values. Therefore, this work makes a significant contribution to the field of energy consumption forecasting, highlighting the ANN as an effective and precise solution for complex data modeling challenges. Additionally, this study leveraged real data from a smart meter, showcasing the practical applicability of the proposed model. The model's 99% accuracy (R\({}^{2}\)) underscores its high reliability and potential for seamless integration across various real-world settings, including residential, commercial, and larger-scale energy distribution systems. This level of precision in both estimation and model validation highlights the robustness of the method, making it a resilient solution for diverse energy management applications in real-world scenarios. Furthermore, the study shows that the ANN model has strong potential for scalability and applicability across different contexts, although future work must address challenges like scalability across larger datasets or complex environments, such as smart cities or industrial zones. As a result, the use of ANN, combined with a comparative analysis of polynomial regression, emerged as the best alternative for producing accurate and efficient predictions while resolving the inherent nonlinearity in the energy consumption problem. This inno \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Neurons Hidden Layer (m \(\times\) 1)** & **Neurons Output Layer (i \(\times\) 1)** \\ \hline
1 & \(-2.9495\) & \\ \hline
2 & \(-3.0871\) & \\ \hline
3 & \(2.9715\) & \\ \hline
4 & \(-0.7846\) & \\ \hline
5 & \(-1.7193\) & \\ \hline
6 & \(-1.5501\) & \\ \hline
7 & \(-1.4256\) & \\ \hline
8 & \(-0.3505\) & \\ \hline
9 & \(0.4647\) & \\ \hline
10 & \(-5.2825\) & \\ \hline
11 & \(7.5961\) & \\ \hline
12 & \(1.3157\) & \\ \hline
13 & \(1.3579\) & \\ \hline
14 & \(-0.8557\) & \\ \hline
15 & \(3.4747\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bias of the neurons of each layer (hidden and output). vative technique not only enriches the field of energy consumption forecasting but also greatly contributes to the creation of more precise and effective solutions to complex real data modeling difficulties. Therefore, the choice of the ANN, combined with the comparative analysis of polynomial regression, stands out as the best option for providing accurate and efficient predictions while addressing the nonlinearity inherent in the energy consumption problem. A.G., H.L.M.d.A., and A.B.N. : conceptualization conceptualization and writing--original draft; A.N.d.S., A.v.F.H., J.C.C., and F.T.N. : writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research is funded by CAPES and CNPq under grant number 88887.704285/2022-00.