基于DDTW聚类和SK TCN-GC BiGRU的分布式光伏短期功率预测  

Short-term Power Prediction for Distributed Photovoltaic Systems Based on DDTW Clustering and SK TCN-GC BiGRU Network

作　　者：段宏 郭成 孙海东 王嵩岭 DUAN Hong;GUO Cheng;SUN Haidong;WANG Songling(School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China;School of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学机电工程学院,云南昆明650500 [2]昆明理工大学电力工程学院,云南昆明650500

出　　处：《智慧电力》2025年第4期71-80,共10页Smart Power

基　　金：国家自然科学基金资助项目(52367002)。

摘　　要：针对分布式光伏短期功率的准确预测问题,提出了一种基于导数动态时间规整算法(DDTW)聚类和复合注意力预测网络(SK TCN-GC BiGRU)的短期光伏功率预测方法。首先,应用DDTW对历史数据进行相似日分析以构建针对性的训练集。其次,结合选择性内核网络(SKNet)和全局上下文模块(GC Block)优化TCN与BiGRU模型,分别增强提取多尺度特征和全局信息的能力。仿真结果验证了所提模型的优越性,尤其在气象条件数据波动较大的情况下,表现出较强的鲁棒性。To address the issues of accurate short-term power prediction for distributed photovoltaic(PV)systems,this paper proposes a hybrid method integrating derivative dynamic time warping(DDTW)clustering and a composite attention prediction network(SK TCN-GC BiGRU).First,DDTW is applied to historical data for similarity day analysis,constructing targeted training sets.Second,the temporal convolutional network(TCN)and bidirectional gated recurrent unit(BiGRU)are optimized by combining a selective kernel network(SKNet)and a global context block(GC Block),enhancing their capabilities in extracting multi-scale features and global contextual information.Simulation results validate the superiority of the proposed model,demonstrating strong robustness under significant meteorological data fluctuations.

关 键 词：短期光伏功率预测 时间卷积神经网络 双向门控循环单元 导数动态时间弯曲聚类 

分 类 号：TM615[电气工程—电力系统及自动化]