Rapotu, Dimakatso RMasoga, Mandla AKaramanski, Stefan2026-01-132026-01-132025-10http://hdl.handle.net/10204/14579Wind forecasting is a critical tool for understanding the long-term capacity of wind energy. As global temperatures rise due to climate change, it is essential to understand how these changes will impact future wind generation. This study presents a physics-based approach to forecasting the effects of climate change on wind energy in South Africa. The analysis leveraged five different global climate models, all using the Representative Concentration Pathway 8.5 (RCP 8.5) scenario, which represents a high-emission future. These climate models provided forecasted wind data from 1960 to 2100 for a specific location. This data was then used in a four-step long-term wind forecasting model. The model calculates wind energy production based on specific turbine characteristics, adjusts for air density and hub height, and then aggregates the total energy output for all turbines available at the site. The model was tested on 35 onshore wind farms in South Africa and validated using power output predictions for [2012,2016,2020,2024] using Sere and Amakhala data acquired from Renewables ninja. The results showed a Pearson correlation of 0.99 for both sites, demonstrating the high reliability of the proposed forecasting method. This approach provides a robust way to assess the long-term viability of wind energy projects under future climate conditions.FulltextenWind energyLong-term forecastingClimate changeClimate modelsConference Presentationn/a