## 1 Introduction
### Literature Review  
A number of studies (see, for example, [PERSON] (2018), [PERSON] et al. (2018), [PERSON] et al. (2018) and [PERSON] et al. (2018)) have been carried out for forecasting global horizontal irradiance in South Africa. A unified approach to solar power forecasting remains a challenge in South Africa. [PERSON] et al. (2018), considered a modelling framework that was based on multiple quantile regression whereas [PERSON] et al. (2018) suggested the application of quantile regression as well as statistical smoothing techniques. Solar power forecasting depends on the use of indicators such as moistness, sun's path, temperature, solar power yard attributes that utilise energy from the sun to deliver solar power and dissipating process. Photovoltaic (PV) cells are used to come up with electric power from the sun's energy. The energy delivered relies upon radiation extracted and on the attributes of the solar panel. This study will benefit the general public by providing forecasting information that is vital for competent use, solar power trading, managing the electricity grid and, improving energy quality supplied to the grid and will assist with decreasing the costs that are identified with climate reliance. Solar power forecasting which is based on several time horizons performs a vital role in health facilities, building conveyance frameworks, research institutions, schools, PV storage structures management and many other systems that depend on energy. It helps power grid operators in accommodating the load to be able to enhance transportation of energy, assign the necessary balance of energy from other sources during the period when solar power is inaccessible, activities to do with maintenance at the solar power plants. When considering time horizon from various minutes to a few hours, i.e., for a very short term time scale, trend models using on-site measurements are adequate. Hourly predictions containing high geographical and physical settlements that are found from ground-based images are also important. Various authors have ventured into solar power forecasting using numerical weather prediction models, time series regression, artificial intelligence and many more. In [PERSON] et al. (2018), the authors forecasted PV yield over the range of forecasting horizon utilising diverse ordered scales. They did a study and reviewed PV forecast models where they looked at different variables used for forecasting it, that is its yield control profile and execution matrices to assess the predicted model. Their research was based on time series regression techniques and artificial intelligence. In [PERSON] et al. (2018), the authors did a study on introducing global energy forecasting in a competition that was done in 2014, a probabilistic estimation of energy using four tracks for the following variables: price, wind, load and sunlight energy-based predictions. The paper aimed at producing decade ahead probabilistic forecasts. A day-ahead prediction of sunlight-based power yield from plants extracting solar energy in America in the Southwest was done in [PERSON] et al. (2018). The study made use of forecasting methods for the day-ahead hourly averaged energy output from solar power plants dependent on optimisation with the use of least squares prediction on weather factors using numerical methods. Three different prediction strategies were assessed against data that were from some two tracking 1 MWp plants that were in California for the years 2011-2014. In [PERSON] et al. (2018), the authors did an analysis applying kernel density forecasting methods, instability prediction models and a mixture of the two models with the main thrust on enhancing the predicting interim performance. An analysis was done using the two methods based on non-parametric kernel density estimation on a minute to minute solar irradiance. To incorporate volatility forecasts, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Single Exponential Smoothing (SES) estimation were used.