Karamanski, StefanGrobler, Jan-HendrikErfort, G2025-03-192025-03-192024-10http://hdl.handle.net/10204/14179The energy mix in South Africa has a rapidly increasing renewable energy penetration, but it is still being dominated by fossil fuels at approximately an 80% share. It is evident that development of renewable energy plants have prolonged durations, especially in the case of wind energy. One of the major preliminary requirements in this development is a wind resource assessment to evaluate the suitability of a potential site. This can be very expensive and difficult. The Wind Atlas of South Africa (WASA) supplies publicly available time-series wind data for South Africa. This research aims to describe a Python-based method of creating accurate desktop wind resource assessments in South Africa using the WASA data as an input. Subsequent energy modelling software requires a time-series of capacity factors from the resource assessment, which is useful in further integrated analyses, such as in energy master plans. The model’s performance is evaluated on capacity factor outputs from Continuum, an open-source wind resource assessment software package. It was found that the Python-based model performs reasonably well, producing a 32.69% capacity factor compared to Continuum’s 35.5% result, obtained from a site in Mpumalanga, South Africa. By creating this model based on publicly available data and open-source coding practices, the wind development process is simplified. The suitability of wind energy as a preferable energy resource as well as the suitability of a potential site is supported by this research. Stakeholders such as wind farm developers, municipalities, planners and energy operators can find value in this research.FulltextenCapacity factor generationReference wind yearPLEXOSHOMERWAsPConference PresentationN/A