基于时空相关性的风电功率超短期自适应预测方法

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。

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

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.