2026-09-24T12:14:24Zhttps://univendspace.univen.ac.za/server/oai/request

oai:univendspace.univen.ac.za:11602/32132026-06-18T01:00:47Zcom_11602_1927com_11602_1914com_11602_1897com_11602_737col_11602_2138col_11602_738

Ravele, T. Sigauke, C. Ndogmo, J. C. Mushadu, Vhonani 2026 2026-06-17T21:55:11Z 2026-06-17T21:55:11Z 2026-05-19 Mushadu, V. 2026. Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms. . . https://univendspace.univen.ac.za/handle/11602/3213 Mushadu V. Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms. []. , 2026 [cited yyyy month dd]. Available from: Mushadu, V. (2026). <i>Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms</i>. (). . Retrieved from Mushadu, Vhonani. <i>"Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms."</i> ., , 2026. TY - Dissertation AU - Mushadu, Vhonani AB - Accurate wind energy forecasting has become crucial for preserving grid stability and guaranteeing a consistent power supply in the light of South Africa’s expanding shift to renewable energy. As they have a direct impact on scheduling, dispatch choices, and reserve allocation, extreme prediction errors in particular cause serious operational and financial issues. This study uses data from a collection of wind farms in South Africa to model shortterm extreme forecast mistakes in wind energy generation. The blended generalised extreme value (bGEV) distribution and extremal mixture models are two sophisticated extreme value modelling frameworks whose predictive accuracy is compared in this study. An additive quantile regression (AQR) model is used to derive wind energy forecast residuals. Both modelling techniques were then used to identify tail behaviour associated with extreme under- or over-prediction. The findings demonstrate that, in comparison to extremal mixture models, the bGEV model o!ers more accurate, dependable, and well-calibrated predictions of severe forecast errors. These results emphasise how crucial strong and adaptable extreme value models are to enhancing operational wind energy forecasting in South Africa. By showing how better modelling of extreme errors will enhance power system planning, lower uncertainty, and facilitate more e!ective integration of wind energy into the national grid, the study further advances the renewable energy industry. To improve prediction accuracy and deepen system-level insights, future research should take into account geographically disaggregated data from individual wind farms. DA - 2026-05-19 DB - ResearchSpace DP - Univen KW - bGEV distribution KW - extreme value theory KW - extremal mixture models KW - forecast errors KW - quantile regression KW - South African wind farms KW - Wind energy forecasting LK - https://univendspace.univen.ac.za PY - 2026 T1 - Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms TI - Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms UR - ER - M.Sc. in Statistics Department of Statistics Accurate wind energy forecasting has become crucial for preserving grid stability and guaranteeing a consistent power supply in the light of South Africa’s expanding shift to renewable energy. As they have a direct impact on scheduling, dispatch choices, and reserve allocation, extreme prediction errors in particular cause serious operational and financial issues. This study uses data from a collection of wind farms in South Africa to model shortterm extreme forecast mistakes in wind energy generation. The blended generalised extreme value (bGEV) distribution and extremal mixture models are two sophisticated extreme value modelling frameworks whose predictive accuracy is compared in this study. An additive quantile regression (AQR) model is used to derive wind energy forecast residuals. Both modelling techniques were then used to identify tail behaviour associated with extreme under- or over-prediction. The findings demonstrate that, in comparison to extremal mixture models, the bGEV model o!ers more accurate, dependable, and well-calibrated predictions of severe forecast errors. These results emphasise how crucial strong and adaptable extreme value models are to enhancing operational wind energy forecasting in South Africa. By showing how better modelling of extreme errors will enhance power system planning, lower uncertainty, and facilitate more e!ective integration of wind energy into the national grid, the study further advances the renewable energy industry. To improve prediction accuracy and deepen system-level insights, future research should take into account geographically disaggregated data from individual wind farms. 1 online resource (xi, 127 leaves) en PDF University of Venda bGEV distribution extreme value theory extremal mixture models forecast errors quantile regression South African wind farms Wind energy forecasting UCTD Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms Dissertation embargo