Abstract
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.
Highlights
The results show that k-nearest neighbour (KNN) achieves good computational complexity reduced by 30% of the state-of-the-art algorithms
Similar to other works done previously, performance measures such as Mean absolute error (MAE) and Root mean square error (RMSE) will be used to select the best model for short-term forecasting of global horizontal irradiance (GHI)
This paper focused on forecasting GHI at one radiometric station in South Africa using high-frequency data obtained from the Vuwani radiometric station (USAid Venda)
Summary
Renewable energy sources (RES) are rising rapidly in different countries, powered by the cost reduction of wind turbines and photovoltaic (PV) panels [1] They are increasingly becoming the future’s dominant energy source, but harvesting them requires an understanding of the mechanisms of their volatility and the ability to predict various environmental processes over a scale ranger [2]. Such understanding of the environment is the key to renewable energy processing, in particular solar and wind power. The ongoing overuse of fossil fuels in the production of electricity continues to inflict environmental problems such as global warming. RESs are environmentally friendly and inexpensive [4]
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