## 2 Literature Review
### Bibliometric Review
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Model** & **Strengths and Limitations** & **Gaps in Literature** \\ \hline SARIMA/X & Captures seasonality and exogenous factors, complementing ARIMA models. Challenges arise when dealing with irregular seasonal components, requiring further refinement for optimal predictive outcomes. & How to address irregular seasonality for improved accuracy. \\ \hline Facebook Prophet & Shows promising results in energy forecasting by incorporating factors such as seasonality and climate variables. Its performance may vary depending on the dataset and contextual factors. & How to optimize Prophet for specific energy demand datasets. \\ \hline Holt–Winters & Captures seasonal patterns and handles non-linear data. However, its adaptability to changing data patterns is limited, and it may struggle to handle disruptions effectively. & How to improve Holt–Winters for handling changing data patterns. \\ \hline TBATS & Exhibits flexibility in accommodating various seasonalities and trends, while also addressing non-normality through techniques like Box–Cox transformation. However, it needs a substantial amount of historical data for accurate predictions and may have limitations for long-term forecasting. Additionally, it operates under the assumption of a stable environment, which could impact its reliability in dynamic contexts. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Strengths and limitations of forecasting models. Some of the models presented in Table 3, and their acronyms, are Regression-SARIMA-generalized autoregressive conditional heteroskedastic (Reg-SARIMA-GARCH), methods based on Box-Cox Transformation Quantile Regression via Normal Distribution (N-BCQR), the Error-Correction State Space Model (ECSTSP), and a hybrid model based on seasonal adjustment (SEEMDGESN). To the best of our knowledge, the study by [38] is the most recent to employ MAPE as an evaluation metric for forecasting energy in Brazil. However, despite its recent publication, other older studies have achieved superior performance in terms of MAPE values [58; 59]. This presents an opportunity for our work to enhance the accuracy of energy forecasting in Brazil compared to recent studies. Also, this study makes several contributions that distinguish our research in relation to these previous works. We propose a time series analysis methodology, which is conducted prior to the actual forecasting. This methodology enables pattern recognition within the data and assists in model selection and model parameters specification, making it applicable beyond energy consumption to any time series domain. Furthermore, the assessment of these forecasting models does not rely solely on accuracy, but also on a rigorous statistical assessment of model fit through the Ljung-Box test on residuals, ensuring robust validation. We also publish predicted data extending up to 10 years into the future, providing an essential benchmark for future validation as actual data becomes available. Moreover, we incorporate a wide range of exogenous factors, such as climatic, industrial, and economic variables, to capture the complex dynamics influencing energy demand in Brazil. ## 3 Materials and Methods

This section describes the historical data used and the statistical analysis performed on the resulting dataset, as depicted in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Year** & **Model** & **MAPE (\%)** & **Target Country** \\ \hline
[52] & 2017 & ARIMA & 5.34 & Ghana \\
[18] & 2022 & FB Prophet & 3.01 & India \\
[53] & 2022 & SARIMA-regression & 2.48 & Australia \\
[38] & 2023 & RNN & 2.40 & Brazil \\
[54] & 2011 & Reg-SARIMA-GARCH & 1.42 & South Africa \\
[55] & 2019 & N-BCQR & 1.40 & Anhui (China) \\ This work & 2024 & SARIMA & 1.38 & Brazil \\
[56] & 2009 & ECSTSP & 1.10 & Taiwan \\
[57] & 2020 & SEEMDGESN & 0.89 & United States \\
[58] & 2015 & Exponential smoothing & 0.85 & Italy \\
[59] & 2019 & Ordinal regression & 0.74 & Greece \\ \hline \end{tabular}
\end{table}