基于深度学习与误差修正的超短期风电功率预测  

ULTRA-SHORT TERM WIND POWER PREDICTION BASED ON DEEP LEARNING AND ERROR CORRECTION

作　　者：李大中[1] 李颖宇 Li Dazhong;Li Yingyu(Department of Automation,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学自动化系,保定071003

出　　处：《太阳能学报》2021年第12期200-205,共6页Acta Energiae Solaris Sinica

基　　金：河北省自然科学基金(F2011502001)。

摘　　要：提出一种基于深度学习与误差修正的超短期风电功率预测方法。首先采用双向门控循环单元网络模型对风电功率进行点预测,提取初步预测误差。其次,采用随机森林算法构造误差模型,对初步预测结果进行修正。最后,采用核密度估计方法对修正后的误差进行概率分布拟合,计算置信区间。利用某风电场数据对风电功率进行多时间尺度预测,通过仿真验证该文方法的有效性和适用性。An ultra-short term wind power prediction method based on deep learning and error correction is proposed in this paper.First,a bidirectional gated recurrent unit network model is established to predict the wind power and errors of the primary model are extracted. Then,based on the primary errors,an error model is constructed using random forest algorithm to correct the primary results.Finally,using the kernel density estimation to fit the probability distribution of the corrected errors,the confidence interval is calculated. Based on the measured data of a wind farm in China,the effectiveness and applicability of the proposed method are verified by the results of multi-time scale wind power prediction.

关 键 词：风电功率 风电场 深度学习 预测误差 双向门控循环单元网络 区间预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]