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

被引：0
|
作者：
Zhao Y. [1 ]
Li Z. [1 ]
Ye L. [1 ]
Pei M. [1 ]
Song X. [2 ]
Luo Y. [2 ]
Yu Y. [2 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Beijing
[2] China Electric Power Research Institute Co., Ltd., Beijing
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2023年 / 51卷 / 06期
基金：
中国国家自然科学基金;
关键词：
self-adaptation; sparsity; spatial correlation; wind farm; wind power forecasting;
D O I：
10.19783/j.cnki.pspc.220850
中图分类号：
学科分类号：
摘要：
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. © 2023 Power System Protection and Control Press. All rights reserved.
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收藏
页码：94 / 105
页数：11
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