Combinatorial method for bandwidth selection in wind speed kernel density estimation

Abstract

Accurate estimation of wind speed probability density at a given site is crucial in maximising the yield of a wind farm. This goal calls for devising probabilistic models with adaptive algorithms that accurately fit wind speed distributions. In this study, a non-parametric combinatorial method is implemented for obtaining an accurate non-parametric kernel density estimation (KDE)-based statistical model of wind speed, in which the selection of the bandwidth parameter is optimised concerning mean integrated absolute error (L 1 error) between the true and hypothesised densities. The proposed model is compared with three popular parametric models and Rule of Thumb-based KDE model using standard goodness-of-fit and statistical tests. Results confirm the suitability of KDE methods for wind speed modelling and the accuracy of the proposed implemented combinatorial method. It is worthwhile mentioning that the implemented procedure is adaptive (i.e. data driven) and robust. Nomenclature Symbols X random variable f X probability density function of X

Renewable Energy Management System: Optimum Design and Hourly Dispatch

“…Non-parametric techniques proved to be more efficient and accurate in probabilistic modeling of wind and solar data than traditional techniques such as the Rayleigh or Weibull distributions for wind speed and the Beta distribution for solar irradiance [46,47]. The adopted KDE uses Unbiased Cross-Validation (UCV) for bandwidth selection in the r th derivative of the KDE "f (r) (x)".…”