Renewable energy forecasting in South Africa

Abstract

Renewable energy forecasts are critical to renewable energy grids and backup plans, operational plans and short-term power purchases. This dissertation focused on forecasting solar irradiance at one radiometric station in South Africa using high-frequency data obtained from the Vuwani radiometric station (USAid Venda). The aim of this dissertation was to compare the predictive performance of the Genetic Algorithm (GA), recurrent neural networks (RNN) and k-nearest neighbour (KNN) models in forecasting short-term solar irradiance where KNN is used as a benchmark model. From the results it is discovered that the RNN is the best forecasting model in terms of the relative mean absolute error (rMAE). The forecasts of the machine learning algorithms combined using convex combination technique and quantile regression averaging (QRA) found that QRA is the best model. Predictive interval widths analysis with 95% level of confidence was performed and the results showed that QRA over RNN is the best model for forecasting solar irradiance when looking at the PICP and PANAW. The Diebold-Mariano test discovered that the tests fall between the -1.96 and 1.96 range, which tells us that it accepts the null hypothesis. The Murphy diagram presented and showed the 95% pointwise confidence intervals. The study will have an impact on the South African power utility decision-makers to align electricity demand and its supply in an efficient way that promotes potential economic growth and environmental sustainability.