摘要
精确的风速预测对于规模化风电并网及系统运行具有重大意义。提出了一种基于快速相关性约简优化K-mediods聚类的双层长短时记忆网络短期风速预测模型。首先,计算各风速序列及其属性序约简优化K-mediods聚类的双层长短时记忆网络短期风速预测模型。即计算各风速序列及其属性序列的相关程度信息熵,运用快速相关性滤波算法进行属性约简,以降低属性维度及删除冗余属性。然后,采用改进K-mediods对约简后的风速数据进行聚类,得到风速关联属性优化序列,保证类内信息准确全面,并利用双层长短时记忆网络挖掘深层特征及短期预测。最后,通过对实际风场风速进行预测,并与实测数据对比,验证了预测模型的准确性及有效性。结果表明,所提方法在风速属性数据的优选方面具有较大优势,通过保留关联紧密的属性信息提高了预测的精度。
Precise wind speed prediction is of great significance for large-scale wind power grid connection and system operation.The article puts forward a K-mediods clustering short-term wind speed prediction model based on the fast correlation reduction optimization.First the entropies of each wind speed attribute sequence and wind speed sequence are calculated.The fast correlation filtering algorithm is used to reduce the attribute dimensions and delete the redundant attributes.Then,the improved K-mediods is used to cluster the reduced wind speed data,and the optimal sequence of wind speed correlation attributes is obtained to ensure the information within the class to be accurate and comprehensive.The double-layer long and short time memory network is used to dig out the deep features and the short-term prediction.Finally,the practical wind speed of the wind field is predicted.Compared with the measured data,the precision and availability of the prediction model is proved.The results show that the method in the article has great advantages in the optimal method of the wind speed attribute data.
作者
潘超
李润宇
蔡国伟
杨雨晴
张永会
PAN Chao;LI Runyu;CAI Guowei;YANG Yuqing;ZHANG Yonghui(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University),Ministry of Education,Jilin 132012,Jilin Province,China;Jilin Baishan Power Plant,Songhuajiang Hydropower Co.,Ltd.,Huadian 132400,Jilin Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2022年第4期1355-1362,共8页
Power System Technology
基金
国家重点研发计划项目(2016YFB0900100)。
