### Related Work
The cross-correlation coefficient between the predicted and measured solar radiation values was 0.947. [PERSON] et al. studied day-ahead photovoltaic power forecasting by utilizing the convolutional LSTM networks [21]. The recorded data, collected continuously for two years, referred to five parameters as inputs to LSTM neural network: temperature, pressure, humidity, wind and speed direction. As result of the power forecasting, the RMSE showed a very acceptable value of 0.0865. However, in long-term forecasting, the efficacy of the proposed models is not investigated. A forecasting model based on Artificial Neural Network (ANN) has been proposed in [22]; it used the temperature, dew point, relative humidity, and wind speed as inputs. The Mean Absolute Percentage Error (MAPE) of 0.53% was achieved for 14 days of prediction. In the same domain of forecasting solar irradiation, the LSTM is widely used. In [23], four deep learning algorithms were independently trained to predict the solar irradiance of Johannesburg city: LSTM, CNN, Convolutional LSTM, and CNN-LSTM hybrid models. Based on obtained results, the Convolutional LSTM provided the best performance with a normalized RMSE of 1.62% (corresponding to a RMSE of 7.18). In [24], the authors used an artificial neural network, CNN, bidirectional and stacked LSTM to predict solar irradiance values. The used parameters are humidity, station and ambient temperature, station altitude, sea level pressure, absolute pressure and wind speed. A dataset from September 2019 to February 2020 was used to obtain a Mean Absolute Error (MAE) of 41.738; the authors concluded that stacked LSTM is the best model for predicting solar irradiance. In [25], the data over four years were used to forecast temperature, precipitation, and wind speed parameters; the obtained MAE value was equal to 0.708. The authors evaluated the forecasting performance of a stacked bidirectional LSTM (SB-LSTM) approach for both day-ahead and week ahead load. They recommended three approaches to further improve the performance of SB-LSTM: increasing the size of the processed dataset, allowing capturing of variations not included in the limited available dataset, and implementing other architectures. Some researchers found that analyzing moving clouds is very useful for predicting the future position of the clouds and sun occlusion using image processing algorithms [26]. The researchers proposed a framework to forecast the solar irradiance changes; by combining image processing and machine learning. CNNs were used for processing whole sky images with 6-month datasets; the obtained RMSE was equal to 6.11. [PERSON] et al. [27] used SVM, Linear Regression (LR) and Neural Network models (NNM) to forecast the solar energy by using collected data every hour from 1 January 2020 to 5 June 2020, in Aguascalientes (Mexico). The authors used weather parameters for the forecasting, such as wind velocity and direction, temperature, pressure, humidity, sunrise time, and sunset time. Different machine learning approaches were used; the multi-layer perceptron (MLP) algorithm gave the best outcome with an MSE equal to 0.2222. In [28], the backpropagation (BP) neural network is used to construct an effective forecasting model of solar radiation. As inputs to the developed model, the authors used weather parameters, specifically the rainfall, air humidity, and clear-air index; the obtained RMSE equals 0.4708. In the literature, few studies were performed in Middle Eastern regions and partially in Jordan. [PERSON] et al. [29] used ANN to forecast solar radiation in central Jordan using a dataset from 15 May 2015 to 30 September 2017. Instead, [PERSON] et al. [30] used ANN to forecast solar radiation in southern Jordan, obtaining an MSE of 0.00237. Furthermore, [PERSON] et al. [31] used ANN to forecast solar radiation in southern, central, and north Jordan. The dataset from 1 January 1999 to 30 September 2019, was related to wind, air temperature and solar radiation. The obtained MAPE values did not exceed the 3% value.