# Forecasting the Distortion in Solar Radiation during Midday
## 1 Introduction and Literature Review
They concluded that the proposed model gives accurate results compared with two other models' higher-order statistics. The authors in [26] proposed an FL model for global solar radiation forecasting using only the air temperature variable. From another point of view, one of the most common techniques used for solar forecasting is the ANN [27]. Several different architectures of ANNs have been viewed in the literature to model solar radiation forecasting. [PERSON]. [28] presented a practical method for 24 h ahead forecasting of solar radiation using multi-layer perceptron ANN. The results showed that the correlation coefficient is about 98-99% for sunny days and 94-96% for cloudy days. [PERSON]. [29] proposed a new model for solar power forecasting which is a combination of two algorithms: recurrent trained ANN and cooperative neuro-evolution algorithm. Two scenarios were considered and developed: univariate which uses previous power data and multivariate which uses two input variables, previous power data and weather conditions. A data set for two years was used for the evaluation of the proposed model. The authors of Ref. [30] proposed an ANN model for solar radiation estimation. Two data sets were used for evaluation of the proposed method. One data set is daily collected data for four years which includes solar radiation, maximum and minimum temperature, relative humidity, and wind speed. The other data set was gathered during February 2004 with 15 min intervals. The results showed that the proposed model could forecast the exact solar radiation. In [31], three methods were compared with each other for solar forecasting: smart persistence, ANN, and random forest. The objective was to forecast the hourly solar radiation from 1 to 6 hours ahead. The findings reveal that the random forest is the most efficient method compared with the other two methods. The authors in [32] proposed ANN models for daily solar global radiation prediction. The input variables were the air temperature, relative humidity, sunshine hours, wind speed, and evaporation. The data set was collected for four years. Two different ANN techniques were applied for modeling purposes: multi-layer perceptron and radial basis function ANN. They concluded that the multi-layer perceptron technique gives better performance than the other technique. From another point of view, some other papers focused on hybrid models for solar radiation forecasting [7; 33]. Hybrid models are a mix of two or more techniques that were discussed above. The mixing of two or more techniques give better performance and increase the overall accuracy of the solar radiation prediction [8; 34; 35]. The authors in [36] utilized the ANN-Fuzzy hybrid (ANFIS) technique to predict the solar radiation. The model was trained using daily solar radiation data for ten years. The results prove the good performance of the proposed model. The authors in [37] utilized the ANFIS technique for daily global solar radiation prediction for three cities in Iran. The following nine input parameters were considered: sunshine duration, maximum, minimum, and average air temperature, relative humidity, water vapor pressure, maximum possible sunshine duration, sea level pressure, and extraterrestrial radiation. The findings show that the selection of the input variables has the greatest impact on accurate solar radiation prediction. Moreover, Ref. [38] proposed the ANFIS technique for solar radiation prediction. The input variables are the monthly mean minimum temperature, monthly mean maximum temperature, and the sunshine period. The obtained results were compared with the experimental results using two indicators: root mean square error and the coefficient of determination. The comparison proved the accuracy of the proposed model to predict the solar radiation. The authors in [39] utilized the particle swarm optimization (PSO) to train ANN for monthly mean daily global solar radiation prediction in Saudi Arabia.