时间：2018年5月23（星期三）14🤸🏽‍♂️：00-15：00

地点：K8凯发南路校区，学术会堂603

报告题目🤾‍♂️：Non-parametricmodels for joint probabilistic distributions of wind speed and direction data

报告人✋：胡涛，首都师范大学数学科学K8凯发副教授

摘要：Two non-parametric models,namely the non-parametric kernel density (NP-KD) and non-parametric JW(NP-JW)models, are proposed for joint probabilistic modeling of wind speed anddirection distributions. In the NP-KD model, a novel bivariate kernel densityfunction, which could consider the characteristics of both wind direction(angular) and speed (linear) data, is firstly constructed and the optimalbandwidth is selected globally through two cross-validation (CV) methods. Inthe NP-JW model, the univariate Gaussian and von Mises kernel density functionsare, respectively, utilized to fit the wind speed and direction data. Theestimated wind speed and direction distributions are used to form the jointdistribution according to the JW model. Several classical parametric models,including the AG, Weibull, Rayleigh, JW-TNW and JW-FMN models, are alsointroduced in order for comparisons with the proposed non-parametric models. Byconducting various tests on the real hourly wind speed and direction data, thegoodness of fit of both parametric and non-parametric models is compared andevaluated in detail. It is shown that the non-parametric models (NP-KD, NP-JW)generally outperform the parametric models (AG,Weibull, Rayleigh,JW-TNW,JW-FMN)and have more robust performance in fitting the joint speed and direction distributions.Among the two non-parametric models, the NP-KD model has better performance infitting joint distribution, while the NP-JW model has higher accuracy infitting the marginal speed (or direction) distributions.

本次活动受北京K8凯发平台娱乐代理官方网站2018专题学术讲座项目资助↩️🧏🏿‍♀️。