Proceedings of the 5 th NA International Conference on Industrial Engineering and Operations Management Detroit, Michigan, USA, August 10 - 14, 2020 © IEOM Society International Kernel Density Estimation of Solar Radiation and Wind Speed for South Africa Thand’uxolo Kenneth Magenuka, Kabeya Musasa, Kayode Timothy Akindeji Faculty of Engineering and the Built Environment Department of Electrical Power Engineering Durban University of Technology Durban, South Africa magenuka@mut.ac.za; musasak@dut.ac.za; timothya@dut.ac.za Abstract Accurately determining the amount of daily solar irradiance is of paramount significance before commencing on any solar energy projects. Similarly, precise approximation of wind speed probability distribution is essential and significant important in renewable applications and as a result a study was performed. The paper offers a nonparametric density estimation technique for solar irradiance and wind speed probability distribution. In literature, several probability distribution functions (pdfs) are tested both for solar irradiance and wind speeds and compared with the proposed nonparametric kernel density estimation method. To judge the performance and correctness of the appropriate modelling distributions, the root mean square error (rmse) and mean bias error (mbe) are used as performance test criteria for pdfs. The results firstly demonstrate that the proposed nonparametric kernel density estimator gives more accurate estimation with better adaptability than the commonly used conventional parametric distribution for both solar and wind. Moreover, the study shows that the commonly used Gaussian and Epanechnikov kernel methods were the most adaptable methods for all stations. This study will play an in important role in the country as the first-hand information in prediction of future renewable projects. Keywords: Statistical distribution, kernel density estimator, root mean square error, mean bias error. 1. Introduction With Africa’s potential to renewable energy investment and fast growing renewable energy technology, Africa has a significant share in energy production in the future. This will assist in minimizing the high dependence on traditional ways of energy generation and in turn help with job creation. Currently, the composition of energy mix in South Africa shows a great dependence on fossil fuels with approximately 80% of the country’s electricity produced from coal. South Africa is relatively in an infant stage in terms of renewable energy systems however, it is growing and according to the country’s medium-long term goals set in 2010, renewables will account about 17 500 MW of the total energy mix by 2030. About 7 000 MW of this renewable target is expected to be phased in and operating by the end of 2020. Moreover, solar energy seems to have a potential for massive roll out for both small scale and large scale in many regions of South Africa and thereby having a significant share in energy production in the future. Renewable energy system has a major role to play in alleviating this high reliance on fossil fuels and other disadvantages as discussed according to department of energy. In modelling these renewable energy generating system, meteorological data for both wind and solar is required. Evaluating the wind and solar energy characteristics together with their ability is the utmost important step for economic viability of these renewable energy projects. Actually, the pdfs of wind speeds and solar irradiance constitute the wind speeds and solar irradiance gathered over a long period. As a result, their information is important for evaluating the wind and solar energy ability of a certain location. A broad literature examination shows that several parametric probability distribution functions have been tested to predict both wind speeds and solar irradiance, which are applied in reliability evaluation of these renewable resources. Probability distribution for modelling global solar radiation of Ibadan, Nigeria was conducted by Ayodele and concluded that logistic distribution presents the best probability distribution in modelling global solar radiation of Ibadan. In a similar fashion, four distribution methods were examined and all four probability functions showed similar results for Hualien and Taitung by Tian Pau Chang in Taiwan. Many other solar radiation probability distributions have been investigated around the different parts of the continent with Weibull distribution function proving to be best distribution model for M’sila region, Algeria Razika and Nabila (2016), Akuffo and Brew- 2876