# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 1 Introduction
Therefore, solar energy forecasts need to be tailored based on specific applications and adjusted using a suitable forecasting approach. The available literature discusses the solar irradiance forecasting of photovoltaic systems (PV), which uses image-based models for GHI forecasting. According to [20],  
Figure 1: Global distribution of solar resources across the map [14]. this method performs admirably when it is used for solar irradiance forecasting in a targeted region. The high temporal and geographical resolutions of sky images enable the capture of cloud motion. In contrast to empirical methods, image-based models extract cloud information from the sky image dataset to ensure the accuracy of GHI forecasting. However, the drawbacks of image-based models include limited access to image datasets, expensive image capture equipment, and expensive image processing [21]. There are four ways to conduct PV power and solar irradiance forecasting: physical approaches, statistical models, artificial intelligence-based techniques, and hybrid approaches. The physical approaches use satellite images and the Numerical Weather prediction (NWP) model to forecast solar irradiance [22]. The NWP model determines the physical state and solar irradiance using a dynamic atmospheric model. Moreover, the NWP model incorporates both geospatial information and meteorological data [23]. Even though physical models are commonly utilized to forecast atmospheric dynamics, their complexity is a major setback. This problem occurs due to the fact that physical models require higher computational resources and more time to handle massive datasets. Consequently, the use of physical models for estimating short-term solar irradiation is considered ineffective [24]. According to the literature, several other statistical models have been utilized, including autoregressive moving averages [25] and dynamic systems with coupled autoregressive features [26]. Moreover, statistical models are effective when using continuous time-series data, even though these data are not constant because of the effects of season and weather changes. Although the use of statistical models is an excellent option for forecasting solar irradiation over time series, they are unable to incorporate non-linearity into the data, hence the minimal accuracy of forecasts [27]. Moreover, it is challenging to forecast solar energy using physical and statistical methods due to the massive amount of weather data involved. There has been a significant increase in interest in solar radiation forecasting research. By examining the data presented in Figure 2 from the Web of Knowledge website (WoS), it is evident that the number of published papers in this field has experienced seven-fold growth over the past decade (2010-2023) compared to the previous decade (2000-2010). Notably, within the last three years alone (2020-2023), there have been 1300 papers published, compared to the 2159 papers published between 2010 and 2019. Recently, the problem of forecasting solar irradiance was addressed using machine learning algorithms, which have shown superior outcomes compared to the statistical and physical approaches related to quality and dependability. One of the superior aspects  
Figure 2: Publication frequency and citations report according to WoS website. of the machine learning method is the fact that it can extract non-complex features from large amounts of high-dimensional data [28], as well as be utilized to extract non-complex features. In [29], the time-dependent characteristics of solar irradiance are identified, and an ensemble-learning-based solar radiation forecasting model is provided. Moreover, types of artificial intelligence techniques used include the extreme learning machine (ELM) [30], the support vector machine (SVM) [31], the wavelet transform [32], deep learning [33], the artificial neural network (ANN) [34], and ensemble learning [35]. Ref. [36] utilized ANN and SVM to forecast solar radiation at two different locations in Saudi Arabia on a tilted surface.