无气象信息条件下基于AGCRN的分布式光伏出力超短期预测方法  

Ultra-short-term Prediction of Distributed Photovoltaic Power Method Based on AGCRN in the Absence of Meteorological Information

作　　者：赵洪山 孙承妍 温开云 吴雨晨 ZHAO Hongshan;SUN Chengyan;WEN Kaiyun;WU Yuchen(Hebei Key Laboratory of Distributed Energy Storage and Micro-grid,North China Electric Power University,Baoding 071003,China)

机构地区：[1]河北省分布式储能与微网重点实验室(华北电力大学),保定071003

出　　处：《高电压技术》2024年第1期65-73,I0002,共10页High Voltage Engineering

基　　金：国家电网公司总部科技项目(5700-202255222A-1-1-ZN)。

摘　　要：针对分布式光伏普遍缺少气象量测装置而导致功率预测精度不足的问题,提出了一种基于自适应图卷积循环网络的分布式光伏出力超短期预测方法,可以在无气象数据的条件下,仅基于历史出力数据实现光伏出力精准预测。首先,分析了光伏出力数据兼具时序性和空间相关性,利用门控循环网络提取时序特征,利用自适应图卷积网络挖掘传统图卷积网络无法捕捉的光伏出力潜在空间相关性。然后,融合门控循环单元和自适应图卷积网络,构建自适应图卷积循环网络以提取多光伏站点出力的时空相关性,并利用注意力机制为不同时刻的时空特征分配权重。最后,通过全连接层输出最终的预测结果。采用某地区屋顶光伏实际出力数据在不同预测时间尺度下比较所提方法与其他方法的预测性能,结果表明,在没有气象数据的情况下,当预测尺度为15、30、60 min时,相比于传统门控循环网络,所提方法的平均绝对误差分别减少了16.9%、19.8%和30.5%。To address the problem of low accuracy for distributed photovoltaic power forecasting due to the general lack of meteorological monitoring devices,this paper proposes an ultra-short-term prediction of distributed photovoltaic power method based on an adaptive graph convolution recurrent network that can realize accurate power output prediction in the absence of meteorological data.Firstly,it is analyzed that the photovoltaic output data have both temporal and spatial correlation.Secondly,the temporal correlations are extracted by gated recurrent unit and an adaptive graph convolution network is utilized to mine potential spatial correlations that traditional graph convolution networks can’t capture.Thirdly,an adaptive graph convolution recurrent network is proposed to extract the temporal and spatial correlations of multiple distributed photovoltaics by combining the adaptive graph convolution network and the gated recurrent unit,and the at-tention mechanism is used to assign weights to the spatio-temporal characteristics at different time.Finally,the final prediction result is output through the fully connected layer.Compared with other methods in different forecasting hori-zons by using the actual photovoltaic output data,the results show that,compared to traditional gate recurrent network,the average absolute error of the proposed method is reduced by 16.9%,19.8%,and 30.5%when the forecasting horizon is 15 minutes,30 minutes,and 60 minutes,respectively.

关 键 词：分布式光伏 超短期预测 时空相关性 无气象信息 自适应图卷积 门控循环单元 

分 类 号：TM615[电气工程—电力系统及自动化]