## 1 Introduction  
In the last decade, solar energy has been of great interest to investors, governments, energy operators, and international organizations. This is because of its multiple environmental and economic benefits. To generate electricity from solar energy, one can either use solar thermal or photovoltaic (PV) panels. However, the PV panel's systems have gained global acceptance and are now playing an important part in providing sustainable clean energy [1]. Consequently, over the past few years, there have been tremendous growths in the PV panel's usage all over the globe. This can be clearly induced from Figure 1, in which the x-axis characterizes the year, and where the y-axis characterizes the amount of power produced from PV panels in Gigawatts (GW). As illustrated in Figure 1, the global capacity of power produced from PV panel's installations increased in two years (2006-2008) from 6.7 GW to 16.0 GW and increase continuously to reach 100.5 GW in the next four years (2008-2012). In total, the installed PV panels' capacity has increased all over the globe from 6.7 GW to 404 GW in the last 12 years. This continuous growth is anticipated to progress at a similar or even at a higher rate in the forthcoming years. This can be related to the technological benefit of the PV panels. In summary, from the year 2006 to 2017, we see an exponential increase in the amount of power produced from the PV panels [2]. This exponential growth of the PV panel's power production, all over the globe, is proportional to the exponential increase in the number of PV panels used to produce such power. However, the power generated from a PV panel depends on a number of uncertain meteorological aspects such as atmospheric temperature, PV panel temperature, and solar irradiance. Thus, the accurate prediction of PV panel's output power production is highly difficult. The unforeseen PV panel's power output creates different negative effects on the quality, the stability, and the reliability of an electrical grid system. These negative effects hinder any possibility for effective distribution scheduling and/or management of the generated power [3; 4]. On the other hand, precise forecasting of the PV panel's output power production can improve the reliability of the system. This could be carried out to keep an effective schedule that would maintain the power quality and stabilization with a grid with secure operation [5]. As a result, a precise prediction of PV power production is of great importance and imposes a rich research area with many challenges. This can be clearly induced by the amount of research that has been carried out to forecast solar power and PV panel's output power through different perspectives. Various methods have been implemented, including numerical weather prediction [2; 6; 7; 8], statistical [2; 9; 10; 11], and artificial neural networks (ANNs) [2; 12; 13; 14; 15; 16]. Several studies have illustrated that ANNs are more appropriate when compared to other techniques; especially when a complicated and non-linear connection occurs between the data without any prior presumption [17; 18; 19]. In addition, because of the excellent outcomes achieved in actual applications [20; 21; 22; 23], ANNs are chosen to be of the most popular models. One great benefit of the ANN model is its ability to model non-linear data associations. The authors in [24; 25] employed a neural network to energy consumption approximation. [PERSON] et al. [26] discussed a procedure to predict the energy consumption of day time load of cooling institutional buildings. [PERSON] et al. [27] developed a multi-layer perceptron for solar radiation forecasting. [PERSON] et al. [16] presented a PV system power output one-hour-ahead forecasting by combining radial basis and wavelet transform function neural network. [PERSON] et al.