| 引用本文: | 赵永宁,李 卓,叶 林,等.基于时空相关性的风电功率超短期自适应预测方法[J].电力系统保护与控制,2023,51(6):94-105. |
| ZHAO Yongning,LI Zhuo,YE Lin,et al.A very short-term adaptive wind power forecasting method based on spatio-temporal correlation[J].Power System Protection and Control,2023,51(6):94-105 |
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| 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。 |
| 关键词: 风电功率预测 空间相关性 自适应 稀疏性 风电场 |
| DOI：10.19783/j.cnki.pspc.220850 |
| 分类号: |
| 基金项目: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) |
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| A very short-term adaptive wind power forecasting method based on spatio-temporal correlation |
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ZHAO Yongning1, LI Zhuo1, YE Lin1, PEI Ming1, SONG Xuri2, LUO Yadi2, YU Yijun2
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1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;
2. China Electric Power Research Institute Co., Ltd., Beijing 100192, China
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| Abstract: |
| 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. |
| Key words: wind power forecasting spatial correlation self-adaptation sparsity wind farm |
