## 2 Literature Review
### Bibliometric Review
In the context of forecasting Italian electricity spot prices, combining seasonal and trend decomposition with Facebook Prophet has been found to improve forecast accuracy and reduce error rates [46]. The model considers factors like seasonality, climatic parameters, and special events to enhance projections. The Holt-Winters model is a time-series forecasting model used in energy demand forecasting. It has several strengths and weaknesses. One of its strengths is that it can capture seasonal patterns in the data, making it suitable for forecasting energy demand with seasonal variations, according to [31]. Additionally, the model can handle nonlinear characteristics in the data, which is significant for accurately predicting energy consumption, as conducted by [47]. However, the Holt-Winters model may have limitations when the data patterns change over time or when there are sudden shifts in the energy demand, expressed by [48]. For ref. [49], it may also struggle to accurately forecast energy demand during significant disruptions, such as the COVID-19 pandemic. Despite these weaknesses, the Holt-Winters model has been shown to outperform other models, such as ARIMA, in terms of accuracy for energy demand forecasting [50]. Furthermore, the TBATS model is characterized by its flexibility in handling various seasonalities and non-linear trends, making it suitable for capturing intricate patterns in energy consumption [32]. Additionally, the incorporation of Box-Cox transformations in TBATS enables it to effectively deal with non-normality in the data, leading to more precise predictions [43]. Moreover, TBATS can handle missing values and outliers commonly encountered in energy demand data without compromising the accuracy of the forecasts [42]. However, certain limitations exist when using TBATS for energy demand prediction. One such restriction is that an adequate amount of historical data is required to accurately capture the seasonal and trend patterns [34]. Furthermore, TBATS might encounter obstacles in generating long-term projections, as it relies on the assumption that future patterns will resemble those observed in the past [42]. Lastly, TBATS is unlikely to perform optimally despite sudden changes or disruptions in the energy system, as it assumes a relatively stable and predictable environment. Table 2 provides a comparative overview of four prominent time series forecasting models: SARIMAX, Facebook Prophet, Holt-Winters, and TBATS. Each model entry details

its strengths and limitations, highlighting its capabilities alongside potential drawbacks. Additionally, the table identifies gaps in the current literature for each model. These gaps point toward potential areas for future research, such as improving SARIMAX's handling of irregular seasonality or optimizing Prophet for specific energy demand datasets. Therefore, the table summarizes the key characteristics of these forecasting models and pinpointing crucial areas for further development and where further research is necessary to optimize their performance in energy demand forecasting. ### Comparison with Other Works

To establish an accuracy comparison base, the literature was searched for studies that used time series forecasting applied to energy. The common accuracy metric used is the Mean Absolute Percentage Error (MAPE), which is better defined in Section 3. Reference [51] states that a MAPE value of less than 10% is considered a Highly Accurate Prediction (HAP), while values between 10% (inclusive) and 20% (exclusive) fall into the category of Good Prediction (GPR). Additionally, predictions with MAPE values between 20% (inclusive) and 50% (exclusive) are deemed Reasonable Predictions (RP), while those equal to or greater than 50% are labeled as Inaccurate Predictions (IPRs). All values presented in Table 3, which detail the accuracy of these studies, are classified as HAP.