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首页> 外文会议>2017 2nd International Conference on Power and Renewable Energy >Research on modeling spatiotemporal correlation of wind power forecast error on multiple wind farms based on Copula theory
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Research on modeling spatiotemporal correlation of wind power forecast error on multiple wind farms based on Copula theory

机译：基于Copula理论的多个风电场风电预测误差时空相关建模研究。

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摘要

The forecast errors of multiple geographically close wind farms have spatiotemporal dependence and this correlation has significant impact to the operation of power system. Therewith, this paper proposes a method to model spatiotemporal correlation of wind power forecast error for multiple wind farms based on Copula theory. Firstly, by comparing fitting accuracy of different fitting methods, KDE-based method with highest fitting accuracy is chose to fit marginal distribution of forecast error. Then, this paper proposes a high dimensional modeling method for short-term wind power forecast error using Copula function and obtains joint cumulative distribution function (JCDF) of forecast errors for multiple wind farms. Finally, the actual forecast error data of four wind farms is used to verily the model. Comparing with the actual dependence structure, the method based on Copula function can effectively model the spatiotemporal correlation and detect independence of wind power forecast errors. Thus the effectiveness of proposed method is proved by simulated results.
机译：多个地理位置紧密的风电场的预测误差具有时空相关性，这种相关性对电力系统的运行具有重大影响。为此，本文提出了一种基于Copula理论的多风电场风电预测误差时空相关建模方法。首先，通过比较不同拟合方法的拟合精度，选择拟合精度最高的基于KDE的方法来拟合预测误差的边际分布。然后，本文提出了一种利用Copula函数对风电短期预报误差进行高维建模的方法，并获得了多个风电场的预报误差联合累积分布函数（JCDF）。最后，使用四个风电场的实际预测误差数据对模型进行验证。与实际的依存关系相比，基于Copula函数的方法可以有效地对时空相关性进行建模，并检测风电预测误差的独立性。仿真结果证明了该方法的有效性。

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