黄晓燕,郭洒洒,陈成优,徐腾翀,韩晓,王涛
HUANG Xiaoyan,GUO Sasa,CHEN Chengyou,XU Tengchong,HAN Xiao,WANG Tao

• 1:国网浙江省电力有限公司台州供电公司
• 2:西华大学电气与电子信息学院

摘要(Abstract)：

当前分布式光伏功率预测多采用静态图模型捕捉分布式光伏电站之间的时空特性，大多未考虑气象因素对不同分布式光伏电站功率预测的影响存在差异。为此，提出一种考虑时空关联及气象耦合的区域分布式光伏功率预测方法。首先，基于对分布式光伏电站出力特性的分析，采用自适应图卷积神经网络和长短期记忆网络挖掘分布式光伏出力的时空特性，并通过非共享参数的神经网络层捕捉不同光伏电站与气象的耦合关系，实现多个光伏电站的功率预测。然后，为减小直接对各个光伏电站预测功率求和带来的误差放大问题，在模型中加入可学习的权重层，得到区域总光伏功率。最后，在多种天气场景下，与多种预测模型进行对比分析，验证了所提预测方法的精确性和稳定性。
Current distributed photovoltaic power forecasting methods typically use static graph models to capture the spatiotemporal characteristics among distributed photovoltaic power stations, but most of them do not account for the varying impact of meteorological factors on the power forecasting of different stations. To address this, this paper proposes a regional distributed photovoltaic power forecasting method that considers spatiotemporal correlation and meteorological coupling. First, based on an analysis of the output characteristics of distributed photovoltaic power stations, an adaptive graph convolutional neural network combined with a long short-term memory network(LSTM) is used to extract the spatiotemporal features of the photovoltaic output. Additionally, a neural network layer with non-shared parameters is employed to capture the coupling relationship between different photovoltaic stations and meteorological factors, enabling the forecasting of power generation across multiple stations. To reduce the error amplification caused by directly summing the predicted power of each station, a learnable weight layer is introduced into the model to obtain the total regional photovoltaic power. Finally, through comparative analysis with various forecasting models under multiple weather scenarios, the proposed method is validated for its accuracy and stability.

关键词(KeyWords)： 分布式光伏;时空关联;气象因素;自适应图卷积神经网络
distributed photovoltaic system;spatiotemporal correlation;meteorological factors;adaptive graph convolutional neural network

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划（2021YFB2601500）;; 国网浙江省电力有限公司科技项目（52SBTZ240156）

作者(Author): 黄晓燕,郭洒洒,陈成优,徐腾翀,韩晓,王涛
HUANG Xiaoyan,GUO Sasa,CHEN Chengyou,XU Tengchong,HAN Xiao,WANG Tao

DOI: 10.19585/j.zjdl.202503009

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