风能间歇性和波动性的特点给电网的平稳运行造成了很大的挑战，导致电网企业限制风电并网，造成弃风行为。因此，实时有效地预测风力发电情况对风电开发和电网的平稳运行至关重要。在分析当前多种预测方法后，提出了基于核主成分分析K均值聚类-门控循环单元(KPCA-K-means-GRU)的短期风电功率预测模型。多维数据能够较好地还原实际物理状态，但过高维度的数据会带来维数灾难。因此，利用非线性的KPCA在保留高维数据信息的同时降低数据维度。随后借鉴负荷预测相似日思路，将降维后的数据通过K-means进行无监督聚类以建立不同的预测模型来提高预测精度。最后分别训练不同类别数据的GRU神经网络参数，进行分类预测以获得更合适的网络模型。

The intermittent and fluctuating characteristics of wind energy pose a great challenge to the smooth operation of power grid, which causes grid enterprises to restrict wind power grid connection, resulting in curtailment behavior. Therefore, the real-time and effective prediction of wind power generation is critical for the development of wind power and the smooth operation of power grid. After analyzing several current prediction methods, a short-term wind power prediction model based on kernel principal component analysis-K-means clustering-gated recurrent unit (KPCA-K-means-GRU) is proposed. Multidimensional data can restore the real physical state better, but data with too high dimensions will cause dimension disaster. Therefore, a non-linear KPCA is used to reduce the data dimension while retaining the information of high dimension data. Then based on the idea of similar days for load prediction, unsupervised clustering of reduced dimension data by K-means is used to establish different prediction models to improve prediction accuracy. Finally, the GRU neural network parameters of different kinds of data are trained separately, and then classification prediction is carried out to obtain a more appropriate network model.

国家自然科学基金项目(61572416)；湖南省自然科学基金项目（2020JJ6009）