摘要
针对风电功率预测数据高维灾难以及云计算的通信依赖问题,提出一种风电功率的属地边端轻量级预测方法。采用KNN算法计算风机间的距离量化空间相关性,并引入轮廓系数自适应地确定近邻数k以降低冗余特征维度,从而确定输入目标预测风机的邻近风机数据;基于Seq2Seq结构的GRU-MLP网络完成各台风机的风电功率预测。实验结果表明,在预测准确率近似的条件下所提方法相较于常规网络复杂度小、效率更高,可为风电场功率预测任务从云端向边缘迁移提供技术方案。
Aiming at the high-dimensional disaster of wind power prediction data and the communication dependence of cloud computing,a lightweight prediction method of wind power is proposed.KNN algorithm is used to calculate the distance quantization spatial correlation between fans,and the contour coefficient is introduced to adaptively determine the nearest neighbor number k to reduce the redundant feature dimension,so as to determine the adjacent fan data of input target prediction fan;GRU-MLP network based on Seq2Seq structure completes the wind power prediction of each fan.The experimental results show that the proposed method has less complexity and higher efficiency than the conventional network under the premise of approximate prediction accuracy,can provide technical solution for the migration of wind farm power prediction tasks from cloud to edge.
作者
崔昊杨
孙昊宇
杨程
王茺
许永鹏
刘诚
CUI Haoyang;SUN Haoyu;YANG Cheng;WANG Chong;XU Yongpeng;LIU Cheng(School of Electronic and Information Engineering,Shanghai Electric Power University,Shanghai 201306,China;School of Materials and Energy,Yunnan University,Kunming 650031,China;School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University,Shanghai 200052,China;Changsha Hongze Power Technology Co.Ltd.,Changsha 410015,China)
出处
《智慧电力》
北大核心
2022年第8期7-13,117,共8页
Smart Power
基金
国家自然科学基金资助项目(52177185)。
