## 1 Introduction  
The utilization of solar energy has increased several fold in recent years due to the drop in pricing of the solar panels and the improvement in the solar panel efficiency. However, integrating this large scale solar yields existing energy supply structures and maximizing the exploitation of such renewable resources are still significant challenges, due mainly to the fluctuating nature of solar radiation at Earth's surface and its high dependence on different atmospheric conditions (e.g., distribution of humidity and clouds). Accurate solar radiation predictions over several time horizons which are essential to grid balancing, real-time unit dispatching, scheduling and trading are required. Intra-hour forecasts can be determined from advection of clouds observed with ground-based all-sky cameras [1]. The use of a satellite image shows acceptable forecasting skill from 30 min up to six hours [2; 3]. For very short-term solar radiation forecasting, statistical approaches that are based on the learning process of historical information have been proposed. These approaches can be divided into two groups, that is, time series and artificial intelligence (AI) based methods. The support vector machines (SVM), linear regressions, autoregressive moving average (ARMA) and the autoregressiveintegrated moving average (ARIMA) derived from ARMA that belong to the first group have been widely applied for different applications of solar radiation prediction [4; 5; 6]. The AI-based methods, in combination with historical data at the site, is also frequently employed to predict solar radiation. Among them, the artificial neural network (ANN) is more popular and robust to respond to sudden changes dynamically [7]. [PERSON] and [PERSON] [8] used it to forecast solar radiation for a target site and a better result compared to an empirical model has been found. Moreover, [PERSON] et al. [9] have developed six ANN-based models to predict solar radiation, and the results indicate that the forecasting skill is dependent on sunshine duration and temperature. Recently, ANN and random forest methods have been used to predict the solar radiation [10]. [PERSON] et al. [11] adopted the deep neural network (DNN) to predict the global solar radiation in the Netherlands, showing that the DNN model is much better than the traditional models with low relative RMSE. A long short-term memory (LSTM) model has also been implemented for predicting day-ahead global horizontal irradiance [12; 13]. Specific mathematical equations and detailed information related to statistical approaches have been summarized by [PERSON] et al. [7]. Day-ahead forecasting usually is vital to grid integration and decision-making in the energy market. Although [PERSON] and [PERSON] [14] proposed a multi-layer perceptron (MLP) model from which the 24-h forecasts of solar irradiance can be obtained, the accuracy of most of the aforementioned statistical approaches decreases rapidly with the extension of the forecasting horizon. Thus, the numerical weather prediction (NWP) model-based approaches become more appropriate and accurate when the prediction horizon is beyond six hours [15; 16]. The approaches based on NWP models can generate forecasts of wind speed, direction, surface temperature, humidity and pressure, which can be regarded as indicators for making appropriate strategies. Several NWP models, for example, the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) [17] and Weather Research and Forecasting (WRF) [18; 19; 20; 16] are widely used in solar radiation prediction. [PERSON] et al. [18] evaluated the reliability of three-day ahead global horizontal irradiance and direct normal irradiance in southern Spain using the WRF model with a spatial resolution of 3 km. Later, [PERSON] et al. [19] adopted the same horizontal resolution to investigate the ability of the WRF model for forecasting solar radiation at Reunion Island.