# Forecasting the Distortion in Solar Radiation during Midday
## 1 Introduction and Literature Review
The input variables of the model are: the month of the year, sunshine period, and longitude and latitude of the site. To prove the performance of the proposed model, it was compared with the ANN trained by the backpropagation algorithm. The authors in [40] proposed three models for monthly solar radiation prediction; ANFIS, hybrid ANFIS, and PSO, and hybrid ANFIS and differential evolution. The input variables are the maximum and minimum air temperature and the sunshine duration. The performances of the three models were compared with other published benchmark models. In addition, the three models were evaluated using root mean square error, co-efficient of determination, and mean absolute bias error statistical indicators. The authors in [41] proposed a hybrid model based on SVM and firefly algorithm (SVM-FFA) for forecasting the monthly mean global solar radiation. The input variables are the maximum air temperature, minimum air temperature, and the sunshine period. The performance and the precision of the proposed hybrid technique were compared with the ANN and the GA techniques. The authors in [33] proposed a new hybrid model for daily global solar radiation forecasting. The proposed hybrid model is a combination of the multi-layer perceptron and the boosted decision tree techniques. A combination of different input variables was used: extraterrestrial solar radiation, daily minimum air temperature, daily maximum air temperature, daily mean air temperature, sunshine duration, and extraterrestrial solar radiation. The experimental results revealed that the high accuracy of the multi-layer perceptron is higher than the other one. This paper proposes a new way in dealing with the problem of forecasting solar radiation. This paper presents a method to classify the global solar radiation into different categories in terms of the distortion/fluctuation in solar radiation so that the power systems operators can determine--with good accuracy--whether the power generation from the PV plants is reliable or not. The importance of predicting the distortion in the midday hours is to avoid the sudden loss of high power generation from PV plants during midday hours and to avoid the unexpected misbalance between the generation and load, which leads to many dangerous situations in power systems. In this paper, we introduce a distortion index (_DI_) that gives information about the fluctuation of global solar radiation during the midday hours (at which the radiation is at its peak and the PV power generation is maximum) by analyzing the solar radiation of the early minutes. Compared with the other prediction techniques, the proposed technique is characterized by its simplicity in modeling and implementation. In addition, the simplicity of the proposed approach makes it suitable for use in slow and low-memory processors. The proposed approach was tested under different real weather data and at different months. Simulation results showed that the proposed approach succeeded in forecasting the distortion of the solar radiation at midday by measuring the early morning radiation with good accuracy.