# Forecasting the Distortion in Solar Radiation during Midday
## 1 Introduction and Literature Review  
Renewable energy resources have become important power sources in the current world of energy. Solar photovoltaic (PV) energy is preferred over other renewable energy resources since it is an environmentally friendly, non-noisy, and non-exhaustible source [1, 2]. The total PV installations in 2017 was 100 GW, which increased by 32% compared with 2016, bringing the global PV energy installations to 408 GW. According to marketing forecasts, the total PV installation will grow from 255.8 GW in 2023 to 347 GW in 2026 [3]. The amount of solar PV power generation mainly depends on the meteorological conditions such as solar radiation, ambient and PV module temperatures, wind speed, humidity, dust, etc. [4]. On the other hand, solar PV energy exhibits randomness generation and finally the total solar PV cost will be higher. Moreover, the use of a huge number of inverters and DC-DC converters may lead to power system instability. All of these problems could be mitigated by the accurate forecasting of solar radiation [1, 5]. Therefore, the installation of new PV plant requires more forecasting research on the effect of meteorological conditions in order to forecast the generated amount of PV. Solar radiation forecasting helps in optimal sizing of the PV plant, which leads to a reduction in the installation and operation costs [6]. Various models have been proposed in the literature to provide accurate solar PV energy forecasting for the next few minutes to the next few days. Some review papers classified the solar radiation forecasting models into empirical and statistical models, based on artificial intelligence techniques, or hybrid models [7; 8]. Empirical models have been classified into four categories: sunshine-based models, temperature-based models, cloud-based models, and other meteorological parameter-based models [9; 10]. Sunshine-based models are the most common models that are used for solar radiation estimation. The sunshine period is an important measure to estimate solar radiation. Most of the sunshine models use a formula called a sunshine ratio to estimate the monthly average daily solar radiation. This formula is a linear relationship between two ratios: the ratio of the measured average daily solar radiation to the theoretical clear day value and the other ratio of the measured average daily sunshine duration to the theoretical maximum possible sunshine duration [11]. This formula, called the Angstrom-Prescott model, was firstly proposed in Ref. [12]. The Angstrom-Prescott model has been improved and optimized to produce more accurate results suitable for different applications and different study areas [11; 13; 14; 15; 16; 17]. Statistical methods are based on driving a relationship between the predictors, input, and output variables [18; 19]. Univariate time series analysis models are based on statistical quantitative analysis to forecast one variable based on past observations. On the other hand, multivariate time series analysis models are based on statistical analysis of more than one variable in the time domain [20; 21]. The authors in [22] proposed three methods based on global horizontal irradiance using closure equation and cloud cover information. The authors in [23] proposed a multivariate forecast model which uses s temperature, humidity, and nebulosity meteorological combinations. Artificial intelligence techniques are software programs used to solve nonlinear complex problems without the need for a mathematical model. These are fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), support vector machine (SVM), extreme learning machine (ELM), etc. The authors in [24] proposed a model based on FL and SVM for horizontal global solar radiation estimation. In this model, linear, polynomial, Gaussian, and sigmoid functions are used for solar radiation estimation. On the other hand, the authors in [25] proposed an FL model for daily global solar radiation prediction in Morocco.