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Renewable energy forecasting in South Africa
MSc (Statistics)Department of StatisticsRenewable 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.NR
Short-Term Solar Power Forecasting Using Genetic Algorithms: An Application Using South African Data
Renewable energy forecasts are critical to renewable energy grids and backup plans, operational plans, and short-term power purchases. This paper focused on short-term forecasting of high-frequency global horizontal irradiance data from one of South Africa’s radiometric stations. The aim of the study was to compare the predictive performance of the genetic algorithm and recurrent neural network models with the K-nearest neighbour model, which was used as the benchmark model. Empirical results from the study showed that the genetic algorithm model has the best conditional predictive ability compared to the other two models, making this study a useful tool for decision-makers and system operators in power utility companies. To the best of our knowledge this is the first study which compares the genetic algorithm, the K-nearest neighbour method, and recurrent neural networks in short-term forecasting of global horizontal irradiance data from South Africa