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.