## 2 An Overview of the Literature on Solar Forecasting  
Current research on solar irradiance forecasting is focussing to a very large extent on the use of machine learning techniques such as artificial neural networks (ANNs) [13] and support vector machines (SVMs) [14]. ANNs can map relationships between various variables given there is available information to learn from [15]. In a study by [16], hybrid neural networks and metaheuristic algorithms are applied to short-term forecasting of solar energy. [PERSON] and [PERSON] [17] used ANN for short-term wind forecasting in La Venta, Oaxaca, and Mexico. [PERSON] et al. [18] proposed the application of a recurrent neural network (RNN), which was based on the control strategy for storage of the battery energy in systems generation with intermittent renewable energy sources. [PERSON] et al. [19] applied a neural network to the short-term load and wind power forecasting based on prediction intervals. [PERSON] et al. [20] suggested a combined autoregressive and dynamic system (CARDS) for the 1 hour ahead global solar irradiance prediction. The suggested approach improves the global solar irradiance forecast accuracy by 30% compared to ANNs models. [PERSON] and [PERSON] [1] used a system of numerical weather predictions (NWP) to conduct their research on enhancing renewable energy forecasting. Throughout their report, they suggested a feature engineering approach to obtain further details for wind and solar energy forecasts from an NWP's grid. They considered that adequate processing of direct NWP data elements can boost the forecasting capability and that the renewable energy forecaster could invest time in this knowledge discovery process. The use of ANN as a new strategy for assessing future energy consumption levels in SA is suggested by [21]. In their study, particle swarm optimisation (PSO) was used to train ANNs. Estimates of the annual electricity demand were determined per scenario, and it was noted that the proposed ANN approach attains a comparatively better energy demand forecast within acceptable errors. [PERSON] [22] discussed the implementation of generalised additive models (GAMs) to the prediction of medium-term demand for electricity utilising data from SA. In the study, the variable selection was carried out using Lasso (least absolute shrinkage and selection operator) via hierarchical interactions. [PERSON] and [PERSON] [23] used autoregressive moving average (ARMA), Neural networks (NNs), and Neuro-Fuzzy (NF) systems to model and forecast non-linear processes. They found that NF has a better ability than ARMA and NNs to model the system, while NNs are better than ARMA. [PERSON] et al. [24] proposed a genetic algorithm (GA) framework using 'negative load' and the 'inclusive' strategy. They emphasized that, as wind power cost for the 'negative load' approach is presumed to be zero, the results do not show the actual cost. The actual cost of wind power should therefore be in the 'zero-fee' strategy. They also discovered that the use of wind power in the economic load dispatch (ELD) computation affects cost, load volatility pictured by some other plants, and reserve necessity because of the expected error. [PERSON] and [PERSON] [25] proposed an RNN-based approach for forecasting the regular production of electricity from a PV power system (PVPS). In the study, three RNN models were compared where the proposed third RNN-II provided more reliable daily forecasts for generating electricity compared with the other proposed MLP and RNN-I models. A combination approach technique for short-term forecasting of solar irradiance and photovoltaic (PV) power is presented in [18]. The solar irradiance prediction was carried out with the help of physical techniques named clear-sky techniques. Short-term PV forecasting is then carried out with the help of an auto-associative kernel regression (AAKR) strategy that is typically implemented for the detection of faults. The findings of the proposed model showed increased efficacy in forecasting solar irradiance.