## 2 Literature Review
### Bibliometric Review  
A bibliometric analysis is conducted to examine the publication domain surrounding electric energy demand forecasting. To support this analysis, the Scopus database is used. The search terms presented in Listing 1 result in 3549 documents that are used in the analysis of this section. ```
(time-seriesOR''timeseries'')AND(forecastORforecastingORpredictingORpredict)ANDenergyAND(electricORelectricalORelectricity)
```  
For a greater similarity with this work, for the following investigations, the documents are filtered to include exclusively those in at least one of the areas of Engineering, Energy, Computer Science, Mathematics, or Decision Sciences. This results in 2882 documents. As the electric energy consumption and production patterns vary among different economies and geographical regions, the forecasting studies usually target a specific country, such as this work, which is focused on Brazilian electric patterns. To assess the frequency at which the BRICS countries are the objects of similar studies, these filtered documents are filtered successively to the presence of each BRICS country in the document title, and the number of publications targeting each of them is depicted in Figure 0(a). China leads the publication count, followed by India and Brazil, with similar values to each other. Despite Brazil being a large country with significant energetic resources, there is little publication targeting it, when compared to China. As this work is based on the SARIMA/X, FB Prophet, Holt-Winters, and TBATS forecasting models, the frequency at which these models are used is also evaluated for both general and energy purposes. To achieve this, Listing 2 shows the search terms used in Scopus, including the filter for energy application and the terms for searching documents that use all models simultaneously. ```
(fbORfacebook)ANDprophet/ANDenergyAND(electricORelectricalORelectricity) (sarimaORsarimax)/ANDenergyAND(electricORelectricalORelectricity)
```  
holtANDwinters/ANDenergyAND(electricORelectricalORelectricity) ```
tbats/ANDenergyAND(electricORelectricalORelectricity)
```

tbatsANDholtANDwintersAND(sarimaORsarimax)ANDprophet

Figure 1: Number of published documents in energy forecasting. The results, shown in Figure 1b, suggest that there is no study in the Scopus base that conducted a comparative assessment between these models simultaneously with any applicability, indicating the novelty of this work. ### Discussion on the Strengths and Limitations of SARIMA, FB Prophet, Holt-Winters, and TBATS Models

The SARIMA model is a powerful tool for energy demand forecasting. It has been used in various studies to forecast electricity consumption, generation, peak load, and installed capacity [42]. It incorporates seasonal factors, whilst Exogenous SARIMA (SARIMAX) also supports additional regression with exogenous variables, which helps improve forecasting accuracy and reduce error values [43]. It outperforms simpler autoregressive integrated moving average-based techniques regarding forecasting accuracy [44]. The SARIMA model is particularly effective in capturing vital seasonality components in demand-related time series [45]. However, SARIMA may face challenges when dealing with irregular seasonal components in electricity demand. Despite this limitation, SARIMA has been shown to significantly improve the accuracy of predictions, leading to better management of electrical distribution. Facebook Prophet is a time-series model used for energy demand forecasting. It has been applied in various studies to predict energy consumption in different sectors, such as manufacturing companies in the food sector [28]. The model has shown promising results in predicting energy consumption of electricity, water, and diesel fuel. However, it is essential to note that the model's performance may vary depending on the specific dataset and context.