## 1 Introduction  
Since it was discovered in the late 1820s and early 1830s, electric power generation has become--and it will continue to be--a relevant energetic vector involved in most developed countries' socio-economic activities [1; 2]. In order to reduce the greenhouse gases associated with electricity production, some studies in the literature point out that it is necessary to decrease the energy intensity of today's electrical-powered devices through new technological advances [3]. However, this efficiency improvement will not be enough to balance the expected growth in demand for energy in the short term [4; 5]. This energy demand increase will be based on how quickly economies develop and societies' desire for a better way of life [6] through new electrical household equipment or full or hybrid electric transport [4; 7]. Moreover, both the increase in energy demand and renewable self-supply installations will increase the volatility in energy demand, making activities carried out by the current power systems' controllers, such as real-time dispatching or stochastic unit commitment, more uncertain [8]. If reversible hydropower electric plants are not taken into account, technical limitations make the development of bulk energy storage systems impossible so, energy demand and generation had to be balanced at every moment to guarantee electric power systems' safety and reliable operation [9; 10]. No matter the power system's size, many decisions, such as real-time dispatch, economic dispatch, or maintenance planning, are based on customer demand [11]. Therefore, to keep grids within the bounds of stability and to compute their operating schedules, power systems' decision makers forecast their customers' energy demand. In addition, these energy demand predictions need to be highly accurate in the short term, when there are a significant percentage of non-schedulable renewable energy sources. A review of the literature [9; 12; 13] suggests that energy systems' decision makers use scheduling algorithms that implement forecasters with different prediction horizons in order to analyze future power demand. Based on recently published studies, energy demand forecasters are categorized as follows in terms of prediction horizon:  
Very Short-Term (VST): predictions computed by these forecasters are used for making real-time decisions about activities such as real-time dispatching or power smoothing [14]. Therefore, predictions are made from few seconds up to one hour ahead [15]. Short-Term (ST): values calculated through these forecasters are applied by power systems' decision makers' to well-known unit commitment and economic dispatch schedule activities [16]. Thus, predictions for such activities must be made from a few hours up to a day ahead [9]. Medium-Term (MT): the values produced by these forecasters are used to analyze weekly power demand fluctuations, as well as to schedule power systems' infrastructures maintenance operations [17]. Hence, forecasters predict customers' energy demand from days up to a few weeks ahead [12]. Long-Term (LT): these forecasters' values are traditionally used to examine the need for new infrastructure in upcoming years in order to be able to meet customers' power demand, as well as for power assessment [9]. Therefore, these forecasters compute a system's power demand requirements from months up to years ahead [13]. Given the expected increase not only in energy demand but also in that demand's volatility, as well as the desire to continue guaranteeing power supply under a system's safety conditions, accurate VST forecasters must be developed [18]. As a consequence, this study analyzes the different VST energy demand prediction models found in the literature to develop our model. Over the last decades, as computation techniques progressed, VST energy demand forecasters did as well, achieving more accurate predictions [19].