Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China

Abstract

A kernel density estimation (KDE) model for the probability distribution of wind speed (PDWS) is proposed in this paper for application to wind energy assessment (WEA) in China. Four bandwidth selectors, including normal scale (NS), plug in, biased cross-validation, and least-square cross validation, are proposed for the KDE model. Popular parametric distribution models are also introduced for comparisons with the KDE models. Based on five-year day-average wind speed data from 698 nationwide wind stations in China, the performance and robustness of both parametric and KDE models were evaluated comprehensively on the regional scale. Wind power density (WPD) and wind turbine power output (WTPO), which are the priorities in WEA, are subsequently calculated based on the estimated PDWS models. The ranking results of four individual metrics and one comprehensive metric indicate that the four KDE models outperform the parametric models in fitting the PDWS. The KDE-NS model performs the best compared to the other three KDE models. In addition to the KDE models, the generalized gamma and generalized extreme values were considered as better parametric models in fitting the PDWS. KDE models also performed well in WPD estimation, especially the KDE-NS model with a mean absolute percentage error (MAPE) value as low as 2%. Some parametric models, i.e., Johnson SB and Wakeby, which are not outstanding in PDWS fitting, however perform well in WPD estimation, and their MAPE values can be controlled to remain within 3%. This indicates that the result of the PDWS is not completely equivalent to that of WPD estimation. The WPD and WTPO in most of China's interior areas are less than 40W/m2 and 1.2 GWh, respectively. In the eastern coastal areas, middle and eastern Inner Mongolia, and some western provinces, the WPD and WTPO are relatively higher, and can reach or exceed 240W/m2 and 3.5 GWh, respectively.