## 2 An Overview of the Literature on Solar Forecasting
A GA approach is used by [26] to achieve optimal wind turbine location for higher production capacity when limiting the number of turbines fixed and land used for every wind farm. They considered two cases, which are: non-uniform wind with variable direction, and the uniform wind with direction. [PERSON] and [PERSON] [27] presented an application of GA rooted optimised feature engineering and machine learning to the forecasting of energy consumption. The work focussed on comparing various forecasting methods including XGBoost, support vector regression, and K-nearest neighbour regressor algorithms. The work looked at various meteorological features which are temperature, wind speed, rain, humidity, and time lags. In this work, it is concluded that combinations of prediction methods yield good results. Table 1 presents a summary of some previous studies on modelling and forecasting solar irradiance using genetic algorithms, recurrent neural networks and K-nearest neighbour. The one-hour and regular wind power generation forecasting using Bayesian neural networks is discussed in [34]. Results from this study show that the NNs based on Bayesian neural networks exhibit comparable predictive performance to maximum likelihood neural networks for both one hour and a day ahead forecasts. [PERSON] et al. [35] used data from Tellerie radiometric station, South Africa for the period between August 2009 to April 2010 for a probabilistic prediction of 24 h ahead of global horizontal irradiance (GHI). They used different techniques in forecasting, including quantile regression averaging (QRA) and machine learning techniques, and it is concluded that QRA gives higher accuracy than the machine learning techniques based on the probabilistic error measures. In a study of the classification of solar energy resources in SA using new index measured solar radiation resources, [5] used eight stations and five distinct groups. Results from the study showed that the solar utility index (SUI) has higher solar resources, which discriminate groups compared to other indices such as the probability of persistence (POPD) and the fractal dimension. Apart from typical interplanetary radiation and hours of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Data** & **Models** & **Main Findings** \\ \hline [PERSON] [28] & France hourly rainfall & Nearest neighbour model. & Empirical results show that the nearest neighbour hill estimator gives the same weight to all largest observations. \\ [PERSON] et al. [29] & Hourly solar & A genetic algorithm-support vector machine & Based on the RMSE and MAPE, GASVM had greater predictive ability compared to SVM. \\ [PERSON] et al. [30] & Sky image data from 2004 to 2020 & KNN and random forest models. & The results show that KNN achieves good computational complexity reduced by 30\% of the state-of-the-art algorithms. \\ [PERSON] et al. [31] & Solar data from 2000 to 2017 & Genetic algorithm and Artificial neural network models. & The results show that GA forecasting is much more convenient and also produces accurate results. \\ [PERSON] et al. [32] & Hourly GHI data from 2015 to 2017 & Xgboost, LSTM RNN and random forest network models. & Deep LSTM is found to be the best model in forecasting one hour ahead GHI. \\ [PERSON] and [PERSON] [33] & Daily solar irradiance data from 1983 to 2019 & LSTM, Bidirectional LSTM, GRU, XGBoost, and CNN LSTM. & Results show that the forecasting tasks of shorter horizons give better accuracy while longer horizons require more complex models. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of some previous studies on modelling and forecasting solar irradiance using GA, RNN, and KNN. sunshine [36], presented a report on the prediction of global solar radiation using multiple linear regression models. The study was undertaken in all nine provinces of South Africa. The study revealed that the use of weather parameters for some locations increases the accuracy and efficiency of solar radiation models.