基于云图特征的超短期光伏发电功率预测模型  

An Ultra-Short-Term Photovoltaic Power Forecasting Model Based on Cloud Features

作　　者：叶畅 柳丹 曹侃 YE Chang;LIU Dan;CAO Kan(State Grid Hubei Electric Power Research Institute,Wuhan 430077,Hubei,China)

机构地区：[1]国网湖北省电力有限公司电力科学研究院,湖北武汉430077

出　　处：《电网与清洁能源》2023年第10期70-79,共10页Power System and Clean Energy

基　　金：国家电网公司总部科技项目(4000-202122070A-0-0-00)~。

摘　　要：云团运动的不确定性使得光伏系统输出功率较难准确估计,从而影响新能源并网的可靠性和经济性。为了有效利用卫星的云观测数据,提出了基于云图特征的超短期光伏发电功率预测模型。利用卷积神经网络对卫星云图进行特征提取,且和通过相关性分析后的4种气象特征进行融合,作为光伏发电功率预测模型输入。在此基础上,通过卷积神经网络解析这些特征之间的空间联系,并使用长短期记忆网络实现对光伏输出功率的时间序列预测。此外,考虑到一个自然日中不同时段数据对预测影响不同,引入多头注意力机制来确定关键时间点与关键特征,由此进一步提高所提模型精度。使用光伏电站实际数据以及对应的卫星云图和天气数据,对所提模型的预测效果进行验证。算例分析结果表明,该模型预测精度高且时效性好,特别对于正午辐照较大以及云团运动波动剧烈的时段,模型仍能保证较高的预测精度。Due to the uncertainty of cloud movement,it is difficult to accurately estimate the output power of photovoltaic systems,thereby impacting the reliability and cost-effectiveness of renewable energy integration.To effectively utilize satellite cloud observation data,this paper proposes an ultra-short-term photovoltaic power forecasting model based on cloud features.Convolutional neural networks are used to extract features from satellite cloud images,which are then fused with four meteorological features through correlation analysis and used as inputs to the photovoltaic power forecasting model.On this basis,the spatial relationships between these features are analyzed using convolutional neural networks,and long shortterm memory networks are employed for time series prediction of photovoltaic output power.Furthermore,considering that data from different time periods within a single calendar day may have varying impacts on the prediction,we introduce a multihead attention mechanism to identify crucial time points and significant features.This further enhances the accuracy of the proposed model in this study.The predictive performance of the proposed model is verified using actual data from photovoltaic power plants,along with corresponding satellite cloud images and weather data.The computational analysis results demonstrate high prediction accuracy and timeliness of the model,particularly during periods of high noon irradiation and intense cloud movement fluctuations,where the model still maintains a high level of prediction accuracy.

关 键 词：光伏功率预测 云图特征 长短期记忆网络 卷积神经网络 注意力机制 

分 类 号：TM615[电气工程—电力系统及自动化]