基于时空相关性的风电功率超短期自适应预测方法  

A very short-term adaptive wind power forecasting method based on spatio-temporal correlation

作　　者：赵永宁[1] 李卓 叶林[1] 裴铭 宋旭日 罗雅迪 於益军 ZHAO Yongning;LI Zhuo;YE Lin;PEI Ming;SONG Xuri;LUO Yadi;YU Yijun(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;China Electric Power Research Institute Co.,Ltd.,Beijing 100192,China)

机构地区：[1]中国农业大学信息与电气工程学院,北京100083 [2]中国电力科学研究院有限公司,北京100192

出　　处：《电力系统保护与控制》2023年第6期94-105,共12页Power System Protection and Control

基　　金：国家自然科学基金项目资助(U22B20117,52207144);国家电网公司总部科技项目资助(5108-202155037A-0-0-00);中央高校基本科研业务费专项资金资助(2022TC087)。

摘　　要：为了充分并有效地利用大量风电场之间的时空相关性,在提高风电功率预测精度的同时保障计算效率,提出一种基于时空相关性的风电功率超短期自适应预测方法。以向量自回归模型为基础,对区域内大量风电场之间的时空相关关系进行表征。为减小因空间信息冗余造成的目标风电场预测模型过拟合,引入稀疏化建模技术来优化参考风电场数据的权重系数。此外,采用递归估计算法对预测模型进行自适应训练。根据最新实测功率数据实时更新预测模型系数,不仅可以动态适应预测环境的变化,还可以分散计算负担。采用某区域内100个风电场的实际数据对预测方法进行分析和验证。结果表明,相对于对比方法,所提出的预测方法具有更高的预测精度,且能够降低对密集型计算资源的需求。To improve wind power forecasting(WPF)accuracy and ensure computational efficiency by fully and effectively using the spatio-temporal correlations between wind farms,a very short-term adaptive WPF method based on spatio-temporal correlation is proposed.Vector autoregression is applied as a basic model to characterize the spatio-temporal correlation.To avoid the over-fitting problem of a target wind farm caused by redundant spatial information,sparse modeling is adopted to optimize the weights of data from reference wind farms.The forecasting model is trained by a recursive estimation algorithm.It updates the forecasting model in real-time according to the latest wind power measurements.The model can adapt to varying environments and reduce the computational burden.A case study is carried out using real data from 100 wind farms over a region.Results show that,in comparison with a set of benchmark models,the proposed method can achieve much higher forecasting accuracy while reducing the requirement for intensive computational resources.

关 键 词：风电功率预测 空间相关性 自适应 稀疏性 风电场 

分 类 号：TM614[电气工程—电力系统及自动化]