Predicting Wind Energy Production in South Africa Using Machine Learning

Abstract

South Africa faces an urgent and escalating energy crisis driven by ageing coal infrastructure, frequent load shedding, and rising electricity demand. Wind energy presents a viable renewable energy alternative with significant potential to alleviate these challenges; however, its inherent variability complicates grid stability and energy planning. Accurate wind energy forecasting is essential for optimising power dispatch, minimising curtailment, and enhancing energy security. Despite advancements, traditional forecasting methods, such as physical models and statistical techniques, struggle to capture the complex and nonlinear nature of wind patterns, particularly in data-scarce environments like South Africa. This study investigates the application of machine learning models to improve wind power forecasting in South Africa, where data constraints and fluctuating meteorological conditions pose unique challenges. The research examines the effectiveness of machine learning in predicting wind energy production and assesses the role of explainable artificial intelligence techniques, such as SHapley Additive exPlanations, in enhancing model transparency and interpretability. Using historical meteorological data and turbine performance records from a South African independent power producer, the study evaluates multiple machine learning approaches to determine their predictive performance. A comparative analysis of different machine learning models highlights the most reliable techniques for wind energy prediction. The findings demonstrate that XGBoost outperforms Random Forest, Decision Tree, and K-Nearest Neighbour. Furthermore, the machine learning methods show a significant improvement over traditional statistical techniques, offering improved predictive accuracy while providing insights into key meteorological and operational factors influencing wind power generation. The integration of explainable artificial intelligence further ensures interpretability, fostering trust and practical usability among stakeholders. This research contributes to the renewable energy forecasting literature by adapting machine learning solutions to a data-scarce environment and emphasising the role of interpretability in real-world adoption. The results provide valuable insights for policymakers, energy planners, and grid operators, supporting South Africa’s transition to a more sustainable and resilient energy future. However, the study's findings may be limited in generalisability and accuracy due to the analysis focusing on a single wind turbine chosen based on data availability rather than representativeness. Consequently, the results may not extend to other wind farms or turbines operating under different geographic or climatic conditions.