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面向光伏发电预测的公开数据集综述 被引量:1

A Review of Public Datasets for Photovoltaic Power Generation Forecasting
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摘要 光伏发电具有间歇性和波动性,光伏发电的精准预测是合理安排运行方式和应对措施、提高电网安全性和可靠性的重要措施。基于数据驱动方法是光伏发电预测研究中的主要技术手段,但研究人员面临实际数据难以获取、数据质量差、数据量不够等难题,光伏发电公开数据集为研究人员提供了数据资源,也提供了各种预测方法对比分析的基础。然而,研究人员在选取光伏发电公开数据集时,缺乏对各种公开数据集全面、深入的了解。文章首先阐述了光伏发电数据集中每种数据的收集方法和应用场景;其次调研总结了国内外现有面向太阳能光伏发电预测的公开数据集的现状,梳理了每个数据集的特点和优缺点;最后提出了促进公开数据集应用发展和建立高质量公开数据集的建议。 Photovoltaic power generation has intermittent nature and volatility.The accurate prediction of photovoltaic power generation is an important measure to reasonably arrange the operation mode and response measures,and improve the safety and reliability of the power grid.The data-driven method is the main technical means in the research of photovoltaic power generation prediction.However,researchers are faced with problems such as difficulty of obtaining actual data,poor data quality and insufficient data volume.The public datasets of photovoltaic power generation provide researchers with data resources and the basis for comparative analysis of various prediction methods.In this paper,the current situation of the existing public datasets for solar photovoltaic power generation forecasting at domestic and abroad is summarized.The features and advantages and disadvantages of each datasets are sorted out.Finally,suggestions are presented to promote the development of the application of open datasets.
作者 张沛 刘金城 张彬 翟苏巍 李文云 ZHANG Pei;LIU Jincheng;ZHANG Bin;ZHAI Suwei;LI Wenyun(School of Electrical Engineering,Beijing Jiaotong University,Haidian District,Beijing 100089,China;Electric Power Research Institute,Yunnan Power Grid Co.,Ltd.,Kunming 650217,Yunnan Province,China;Yunnan Power Dispatching and Control Center,Kunming 650011,Yunnan Province,China)
出处 《电力信息与通信技术》 2023年第8期16-21,共6页 Electric Power Information and Communication Technology
基金 中国南方电网公司科技项目“源网荷储一体化协同控制框架研究”(YNKJXM20222456)。
关键词 光伏发电预测 公开数据集 站点数据 数值天气预报 卫星图像数据 photovoltaic power generation forecasting public datasets site data numerical weather forecast satellite cloud image data
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  • 1王育飞,付玉超,薛花.计及太阳辐射和混沌特征提取的光伏发电功率DMCS-WNN预测法[J].中国电机工程学报,2019,39(S01):63-71. 被引量:30
  • 2陈振宇,刘金波,李晨,季晓慧,李大鹏,黄运豪,狄方春,高兴宇,徐立中.基于LSTM与XGBoost组合模型的超短期电力负荷预测[J].电网技术,2020,44(2):614-620. 被引量:205
  • 3刘倩,崔晨,周杭霞.改进型SVM多类分类算法在无线传感器网络中的应用[J].中国计量学院学报,2013,24(3):298-303. 被引量:8
  • 4Femia N, Petrone G, Spagnuolo G, et al. Optimization of perturb and observe maximum power point tracking method[J]. IEEE Transactions on Power Electronics, 2005, 20(4): 963-973.
  • 5Kim I S, Kim M B, Youn M J. New maximum power point tracker using sliding-mode observer for estimation of solar array current in the grid-connected photovoltaic system[J]. IEEE Transactions on Industrial Electronics, 2006, 53(4): 1027-1035.
  • 6Xiao W, Lind M G J, Dunford W G, et al. Real-time identification of optimal operating points in photovoltaic power systems[J]. IEEE Transactions on Industrial Electronics, 2006, 53(4): 1017-1026.
  • 7Chakraborty S, Weiss M D, Simoes M G. Distributed intelligent energy management system for a single-phase high-frequency AC microgrid[J]. IEEE Transactions on Industrial Electronics, 2007, 54(1): 97-109.
  • 8Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system[C]. IEEE Power Engineering Society General Meeting, 2007.
  • 9Tsikalakis A G, Hatziargyriou Nikos D. Centralized control for optimizing microgrids operation[J]. IEEE Transactions on Energy Conversion, 2008, 23(1): 24 1-248.
  • 10Kem E C, Culachenski E M, Ken G A. Cloud effects on distributed photovoltaic generation: slow transients at the gardner, massachusetts photovoltaic experiment[J]. IEEE Transactions on Energy Conversion, 1989,4(2): 184-190.

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