## 1 Introduction  
Global warming is a modern crisis due to the vast consumption of fossil fuels for the generation of electricity. Therefore, renewable energy resources are a potential solution to this problem in reducing the amount of carbon dioxide (CO\({}_{2}\)) emissions as well as keeping the pollution level under control [1]. Solar energy is one of the most promising energy resources due to its wide-ranging benefits of being a clean, cost-free and abundant source [2]. For these reasons, the use of solar energy in electric power generation is in demand as part of efforts to address these environmental issues. However, the production of output power by photovoltaic (PV) panels is unreliable and stochastic as it is influenced by various environmental factors such as surrounding temperature, solar radiation, shading, humidity and wind speed. These are among the challenges that electric grid operators have to face in order to manage the electricity supply system efficiently [3]. To overcome these challenges, several strategies have been developed to balance consumption and generation of electric power. Forecasting of demand on the loads in the short term is one of the strategies to improve operation stability of power systems [4]. The other strategy, which is addressed in this paper, consists of power forecasting using renewable energy resources. Solar power forecasting is essential for energy trading companies and dispatching centres of power networks to make accurate decisions on specific vital issues such as scheduling arrangements and operational control of the power system [5]. On top of that, a precise solar power forecasting improves the reliability and power quality of the overall power system [6]. Generally, solar power forecasting can be classified as direct and indirect forecasting. Direct forecasting predicts solar power data as the model output. On the other hand, indirect forecasting generates the forecasted values of solar radiation. Consequently, the forecasted values of solar radiation are being utilized in PV performance models for deriving solar power production [7]. In the solar power forecasting field, there are two main categories of forecasting methods, namely, physical methods and statistical methods. The physical methods forecast the factors (solar radiation or temperature) that directly influence the production of solar power. Then, the predicted value of solar power is obtained by utilising the forecast result as the input of the physical methods [8]. Otherwise, the statistical methods are built based on historical data and machine learning algorithms to directly forecast solar power without using any physical methods [9]. In literature, researchers have proposed different types of machine learning algorithms for forecasting purposes. The function of the machine learning algorithms is to find the relationship between the input and output values [9]. Multi-layer perceptron (MLP) is a type of artificial neural network (ANN) approach which is a highly competitive method for solar energy forecasting. The comparison studies between the MLP model with other forecasting strategies were done by previous works. [PERSON] et al. compared the MLP model with the Klucher and isotropic sky models [10]. The outcomes showed that there was a good agreement between the forecasted values and actual values of solar radiation, which represents the outstanding performance of the MLP model. Another work was made by [PERSON] et al. for solar power forecasting and the results indicated the superiority of MLP when compared with the multiple linear regression (MLR) and persistence model [11]. [PERSON] et al. utilized a K clustering algorithm to determine the center of the activation function for the radial basis function neural network (RBFNN) model [12]. [PERSON] et al. employed the RBFNN as a forecasting model of PV power in China, and the results showed a lower error percentage during normal and sudden weather changes, showing the ability of the proposed RBFNN to achieve a higher forecasting accuracy [13].