Kekana, HLandwehr, Gregory B2019-06-282019-06-282019-06Kekana, H. and Landwehr, Gregory, B. 2019. Wind capacity factor calculator. Journal of Energy in Southern Africa, vol. 30(2): 118-1251021-447X2413-3051https://journals.assaf.org.za/index.php/jesa/issue/view/253http://hdl.handle.net/10204/11013This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Copyright remains with the author(s). Publishing rights remain with the author(s).The wind capacity factor calculator is developed to perform two main tasks: to estimate the annual energy production from the wind resource at any location in South Africa, and to compare the two datasets used in its operation with standard error analysis to determine whether both datasets are suitable for use. This paper focuses on how the software was developed and on error analysis between the CSIR PV/ wind aggregation study data and the latest Wind Atlas for South Africa data. The results will indicate the way forward after determining whether the error found between the two datasets is significant enough to replace the former with latter, going forward.enCapacity factorEfficiencyMean absolute errorPearson Correlation CoefficientWind Atlas for South AfricaWind energyArticleKekana, H., & Landwehr, G. B. (2019). Wind capacity factor calculator. http://hdl.handle.net/10204/11013Kekana, H, and Gregory B Landwehr "Wind capacity factor calculator." (2019) http://hdl.handle.net/10204/11013Kekana H, Landwehr GB. Wind capacity factor calculator. 2019; http://hdl.handle.net/10204/11013.TY - Article AU - Kekana, H AU - Landwehr, Gregory B AB - The wind capacity factor calculator is developed to perform two main tasks: to estimate the annual energy production from the wind resource at any location in South Africa, and to compare the two datasets used in its operation with standard error analysis to determine whether both datasets are suitable for use. This paper focuses on how the software was developed and on error analysis between the CSIR PV/ wind aggregation study data and the latest Wind Atlas for South Africa data. The results will indicate the way forward after determining whether the error found between the two datasets is significant enough to replace the former with latter, going forward. DA - 2019-06 DB - ResearchSpace DP - CSIR KW - Capacity factor KW - Efficiency KW - Mean absolute error KW - Pearson Correlation Coefficient KW - Wind Atlas for South Africa KW - Wind energy LK - https://researchspace.csir.co.za PY - 2019 SM - 1021-447X SM - 2413-3051 T1 - Wind capacity factor calculator TI - Wind capacity factor calculator UR - http://hdl.handle.net/10204/11013 ER -