## 2 Literature Review
### Bibliometric Review
In the forecasting with exogenous variables, SARIMAX achieved the best accuracy, achieving 1.28% MAPE compared to 1.62% from Facebook Prophet. The smoothed version of the time series resulted in autocorrelation within the residuals for all models. These findings may support the Brazilian government in the elaboration of energy, economy, and sustainability policies. Additionally, the methodology proposed in this \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Average (s)** & **Margin of Error (s)** \\ \hline SARIMA & 2.634 & 0.392 \\ FB Prophet & 0.239 & 0.031 \\ Holt–Winters & 0.031 & 0.008 \\ TBATS & 147.268 & 10.573 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Time performance for forecasting with 215 months as the training window size. The margin of error was calculated with a confidence level of 98% and \(n=25\). study may also be used as a framework for assessing and predicting energy consumption patterns worldwide. As part of future work, we propose evaluating new categories of exogenous variables, such as demographic data, and their relationship to energy consumption patterns. We also propose expanding the statistical comparison to more recent time series models, such as TFT and N-BEATS, possibly to energy generation instead of demand. [PERSON], [PERSON] and [PERSON] conducted the experiments and formal analysis. [PERSON] curated the data. [PERSON], [PERSON] and [PERSON] supervised the experiment and the proposed methodology. All authors contributed equally to writing and revising the document. All authors have read and agreed to the published version of the manuscript. This research was funded by the University of Brasilia (UnB). Not applicable. We used publicly available data published by the Brazilian institutions EPE, ABVE, INMET, and IPEA. The authors acknowledge the technical and computational support from the LATTUTDE Laboratory at the University of Brasilia, to TED 01/2019 from the Attorney General's Office (Grant AGU 697,935/2019), to TED 01/2021 from the National Secretariat for Social Assistance--SNAS/DGSUAS/CGRS for the SISTER City Project--Safe and Real-Time Effective Intelligent Systems for Smart Cities (Grant 625/2022), to the \"Project for Project Control and Unification for the Federal District Government--Sispro-DF\" (Grant 497/2023), to the Dean of Research and Innovation--DPI/UnB, and to FAP/DF. The authors would also like to thank the Brazilian National Confederation of Industry (CNI) for partially supporting this project and for their support and collaboration throughout this research project. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ABVE & Brazilian Association of Electric Vehicles \\ ADF & Augmented Dickey-Fuller \\ ARMA & Autoregressive Moving Average \\ ARIMA & Autoregressive Integrated Moving Average \\ ENCPI & Extended National Consumer Price Index \\ EPE & Brazilian Energy Research Company \\ FB & Facebook \\ GDP & Gross Domestic Product \\ GMPI & General Market Price Index \\ GPI-DA & General Price Index-Domestic Availability \\ IPEA & Brazilian Institute for Applied Economic Research \\ MAPE & Mean Absolute Percentage Error \\ NCPI & National Consumer Price Index \\ SARIMA & Seasonal Autoregressive Integrated Moving Average \\ SARIMAX & SARIMA with exogenous variable \\ TBATS & Trigonometric Seasonality Box-Cox transformation, ARMA errors, \\  & Trend, and Seasonal components \\ TFT & Temporal Fusion Transformer \\ \end{tabular} 

## References

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