基于卫星云图特征区域定位的超短期光伏功率预测方法  

Ultra-short-term Photovoltaic Power Prediction Method Based on Satellite Image Feature Region Positioning

作　　者：司志远 杨明[1] 于一潇 丁婷婷[1] SI Zhiyuan;YANG Ming;YU Yixiao;DING Tingting(School of Electrical Engineering,Shandong University,Jinan 250061,China)

机构地区：[1]山东大学电气工程学院,济南250061

出　　处：《高电压技术》2021年第4期1214-1223,共10页High Voltage Engineering

基　　金：国家重点研发计划(促进可再生能源消费风电/光伏预测技术与应用)(2018YFB0904200)。

摘　　要：卫星可见光云图中的信息可以用来量化云的运动以及厚薄情况,已逐步被应用于光伏功率预测领域。为应对卫星云图处理过程中公式参数的选择大多基于人工经验的问题,提出了一种经验参数的通用选择方法,并在此基础上建立了一种基于卫星云图特征区域定位的超短期光伏功率预测模型,以在云图中精准地实现对遮挡太阳光线的云区域的定位。首先,通过对卫星可见光云图进行标准化处理及去底化处理,去除了可见光云图的日内差异性特征;其次,基于云图区域定位算法,在云图中实时定位云遮挡区域,并通过卷积神经网络获取云遮挡影响特征;最后,融合云遮挡特征与其他影响因素,建立其与光伏功率的映射关系以实现预测。结果表明:所提模型可有效解决云图的日内差异性问题并实现云图特征区域的精准定位,且模型展示出了较好的预测性能。论文研究可为基于云图的光伏预测提供参考。The information in satellite visible images can be used to quantify the cloud movement and thickness, and it has been gradually applied to the field of photovoltaic power prediction. To solve the problem that the selection of formula parameters in satellite cloud image processing is mostly based on artificial experiences, we proposed a general selection method of empirical parameters. On this basis, we established an ultra-short-term photovoltaic power prediction model considering the satellite image feature region positioning, aiming to accurately realize the cloud region localization of blocking the sunlight. Firstly, the satellites visible images need to be standardized and bottom-removed to remove the intraday difference of satellite visible images. Then, based on the feature region positioning algorithm, the occlusion region in the satellite images is located in real time, and the influencing characteristics of cloud occlusion are obtained through the convolutional neural network. Finally, the cloud occlusion characteristics are fused with other influencing factors, and the mapping relationship with photovoltaic power is established to achieve the prediction. The results show that the proposed model can be adopted to effectively solve the problem of intraday difference of the satellite visible images and can realize the accurate positioning of the cloud feature area, and the model shows better prediction performance. The research in this paper can provide a reference for photovoltaic prediction based on cloud images.

关 键 词：卫星云图 图像处理 太阳位置 区域定位 光伏预测 深度学习 

分 类 号：TM615[电气工程—电力系统及自动化]