分布式能源2025，Vol.10Issue(5)：41-51,11.DOI:10.16513/j.2096-2185.DE.25100101
基于协同聚类与优化分解的时序卷积双向神经网络短期光伏功率预测方法
Short-Term Photovoltaic Power Forecasting Based on Collaborative Clustering and Optimized Decomposition for Temporal Convolutional Bidirectional Neural Networks
摘要
Abstract
To address the issue of low prediction accuracy in photovoltaic power generation caused by the intermittency and volatility resulting from weather changes,this paper proposes a multi-level short-term photovoltaic power forecasting method.The method is based on collaborative clustering using self-organizing map and K-means algorithm(S-Kmeans),and an improved artificial lemming algorithm(IALA)-optimized variational mode decomposition(VMD),combined with a temporal convolutional network(TCN)and bidirectional gated recurrent unit(BiGRU).First,key meteorological factors are selected through correlation analysis,and photovoltaic data is classified into three typical weather conditions-sunny,cloudy,and rainy by using the S-Kmeans co-clustering method.Then,the IALA is employed to adaptively optimize the VMD parameters,enabling optimal decomposition of the photovoltaic power series and capturing local signal features more effectively.Finally,a TCN-BiGRU model is constructed for each subsequence,and the prediction result is obtained through component forecasting and global reconstruction,thereby improving prediction accuracy.Experimental results show that the proposed model outperforms the comparison models across all performance metrics under various weather conditions,validating its effectiveness in short-term photovoltaic power forecasting.关键词
光伏功率预测/变分模态分解(VMD)/改进人工旅鼠算法(IALA)/时序卷积网络(TCN)/双向门控循环单元(BiGRU)Key words
photovoltaic power prediction/variational mode decomposition(VMD)/improved artificial lemming algorithm(IALA)/temporal convolutional network(TCN)/bidirectional gated recurrent unit(BiGRU)分类
能源与动力引用本文复制引用
张紫格,舒征宇,刘颂凯,姚钦,童华敏..基于协同聚类与优化分解的时序卷积双向神经网络短期光伏功率预测方法[J].分布式能源,2025,10(5):41-51,11.基金项目
国家自然科学基金项目(52407118)This work is supported by National Natural Science Foundation of China(52407118) （52407118）
