## 1 Introduction
Depending on the type of model used to develop a forecaster, VST energy demand forecasters can be grouped into autoregressive (AR) or artificial intelligence (AI) models [15; 16]. Autoregressive models are based on fixing relationships between selected parameters through the use of the chosen parameters' time-series databases [20]. The main drawback of these models is their limited capacity to predict sudden changes in non-linear systems, as occurs in energy demand or renewable power generation. Hence, AR models must be combined with AI models in order to be able to predict energy demand with sufficient accuracy [21; 22]. Most AR models are of three types: persistence models, which are commonly used as a benchmark in evaluating the results of newly developed forecasters, autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) forecasters. AI models, which are also known as machine learning models, use bigger databases than AR models do. Additionally, unlike AR models, AI models are able to forecast sudden changes in non-linear systems. However, AI models require more time-series information than AR models in order to properly establish relationships between the chosen parameters [22; 23], so greater computational resources are needed. Because AI models are able to predict linear and non-linear systems' parameters with high standard of accuracy, they have been used in fields, such as renewable generation and energy demand prediction [8; 15]. Although AI models are classified in several groups, most developed forecasters rely on using as baseline artificial neural network or support vector machine models [22; 24]. In addition, some studies in the literature suggest that combining single approaches increases forecasting accuracy. This choice, which is known as hybridization or ensembling, is commonly applied to ST forecasters or longer prediction horizons [9; 24], as it must be keep in mind that the longer the horizon, the greater the models' error [25; 26]. While some studies suggest that ensemble models should be developed for VST forecasters, other studies that have developed single approaches achieve similar results to those provided by ensemble models [27; 28]. After reviewing and contrasting the results of these studies, we decided to develop a single approach AI forecaster as our model's baseline. Traditionally, researchers have focused on the development of prediction point forecasters (PPF), which only produce a single value for the chosen prediction horizon. Nevertheless, recent publications in literature claim that PPFs will have little accuracy improvement in the short term and that their partial information will not be enough to accurately predict volatility in future energy demand [29; 30]. Therefore, some authors proposed prediction interval forecasters (PIF) as a solution to improve the quality of the provided information, making it more reliable for a grid's decision makers' [31; 32]. Even though PIFs have been proposed recently, their suitability has been analyzed for the energy demand [32] and renewable generation prediction [30; 31] research fields. From the few studies available in literature, PIFs can be organized along two dimensions. The first dimension classifies forecasters by examining whether a PPF is applied as a baseline to calculate the interval [25; 33], whereas the second dimension relates to whether researchers assume that the behavior of the parameters involved in the forecasters can be expressed through a probability density function (PDF) computed with the variables' historical databases [34]. The first dimension splits PIFs into direct or indirect PIFs, denoted as DPIFs and IPIFs, respectively. While IPIFs are based on the use of a predicted value calculated through a PPF as a baseline for the interval's value prediction, DPIFs predict the interval directly without the use of a PPF value.