Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China
Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China
复制标题
适用于中国风能评估的风速概率分布核密度估计模型
DOI：
10.1016/j.rser.2019.109387
复制
发表时间：
2019-11-01
影响因子：
15.9
通讯作者：
Chu, Fulei
中科院分区：
文献类型：
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作者：
Han, Qinkai;Ma, Sai;Chu, Fulei
中国风能评价（WEA）应用的核心。针对KDE模型，提出了四种带宽选择器，包括标准尺度（NS）、插入、偏置交叉验证和最小二乘交叉验证。还介绍了流行的参数分布模型，以便与KDE模型进行比较。基于698个全国风电场5年日平均风速数据，在区域尺度上对参数模型和KDE模型的性能和稳健性进行了综合评价。在此基础上，计算了WEA中优先考虑的风电功率密度（WPD）和风电机组输出功率（WTPO）。4个单项指标和1个综合指标的排序结果表明，4种KDE模型在拟合PDWS方面优于参数模型。与其他三种KDE模型相比，KDE- ns模型的性能最好。除KDE模型外，广义伽玛和广义极值模型被认为是拟合PDWS的较好参数模型。KDE模型在WPD估计中也表现良好，特别是KDE- ns模型的平均绝对百分比误差（MAPE）值低至2%。一些参数模型，如Johnson SB和Wakeby，在PDWS拟合方面并不突出，但在WPD估计方面表现良好，其MAPE值可以控制在3%以内。这说明PDWS估计的结果与WPD估计的结果并不完全等价。中国大部分内陆地区的WPD和WTPO分别小于40W/m(2)和1.2 GWh。在东部沿海地区、内蒙古中东部和西部部分省份，WPD和WTPO相对较高，分别可达到或超过240W/m(2)和3.5 GWh。
A kernel 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/m(2) 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/m(2) and 3.5 GWh, respectively.