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基于 Copula 理论的风速预测新方法。

A new method for wind speed forecasting based on copula theory.

机构信息

Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210023, China.

Shandong Electric Power Engineering Consulting Institute Corp, Ltd., Jinan 250013, China.

出版信息

Environ Res. 2018 Jan;160:365-371. doi: 10.1016/j.envres.2017.09.034. Epub 2017 Nov 5.

Abstract

How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments.

摘要

如何确定代表风速在风能资源评估中至关重要。准确的风能资源评估对风电场的发展很重要。通常使用线性回归来获得代表风速。然而,风电场的地形灵活性和风速站点之间的长距离通常导致相关性低。在这项研究中,使用 copula 方法通过解释当地风电场和气象站之间的相互作用来确定风电场的代表风速。结果表明,该方法不仅可以通过 Kendall's tau 确定局部测风塔与附近气象站之间的关系,还可以在不假设变量独立的情况下确定联合分布。此外,通过条件分布可以更合理地获得代表风速数据。我们希望本研究能为准确的风能资源评估提供科学参考。

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