基于SOM聚类和二次分解的BiGRU超短期光伏功率预测  

ULTRA-SHORT-TERM FORECASTING METHOD OF PHOTOVOLTAIC POWER BASED ON SOM CLUSTERING,SECONDARY DECOMPOSITION AND BiGRU

作　　者：董雪 赵宏伟 赵生校 卢迪 陈晓锋 刘磊[3] Dong Xue;Zhao Hongwei;Zhao Shengxiao;Lu Di;Chen Xiaofeng;Liu Lei(Key Laboratory of Far-Shore Wind Power Technology of Zhejiang Province,Hangzhou 311122,China;Power China Huadong Engineering Corporation Limited,Hangzhou 311122,China;University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]浙江省深远海风电技术研究重点实验室,杭州311122 [2]中国电建集团华东勘测设计研究院有限公司,杭州311122 [3]中国科学技术大学,合肥230026

出　　处：《太阳能学报》2022年第11期85-93,共9页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(U19B2044,U1865102,61836011);安徽省重点研究与开发计划(202004h07020015)。

摘　　要：提出一种基于自组织映射网络(SOM)聚类和二次分解的双向门限循环网络(BiGRU)超短期光伏功率预测方法。首先利用SOM聚类方法将输入数据进行天气分型聚类,以应对不同天气状态对光伏功率输出特性的影响;然后采用奇异谱分析和变分模态分解相结合的二次分解方法进行原始信号分解,减少信号的波动性,降低光伏数据特征映射的复杂度;最后将分解后的信号作为输入,采用BiGRU网络进行时序信息建模,有效结合不同时刻的信号特征,进一步提升功率预测的准确率。与其他几种经典方法相比,该文方法有效提升光伏功率预测的效果。A BiGRU ultra-short-term photovoltaic power forecasting method based on SOM clustering and secondary decomposition was proposed in this paper. To reduce the influence of different weather conditions on the characteristics of photovoltaic power output,SOM clustering was used to classify the input data. Then,a secondary decomposition method combining singular spectrum analysis and variational modal decomposition was adopted to decompose the original signal aiming to reduce the volatility of the original signal and the complexity of photovoltaic data feature mapping. Finally,the BiGRU network was built by time series modeling with the decomposed signal as input. The training strategy combined the signal characteristics at different times significantly improves the accuracy of the power prediction. Compared with several other classical methods,the proposed method can effectively improve the forecasting performance of photovoltaic power.

关 键 词：光伏功率 分解 自组织映射网络 双向门限循环网络 超短期 

分 类 号：TM615[电气工程—电力系统及自动化]