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太阳能作为可再生能源中最丰富的资源之一，具有巨大的发展潜力，尤其是在当前我国能源结构调整的背景下，其开发与利用显得尤为重要。光伏（photovoltaics,PV）发电是目前应用最为广泛的太阳能利用方式之一。然而，光伏发电的效率和稳定性受到自然因素的显著影响，例如辐照度的高低和云层的变化等因素都会导致光伏功率的昼夜波动。为提高光伏功率预测的精度，本文提出了一种基于地基云图与卫星云图相结合的光伏功率预测方法。该方法通过卷积神经网络（convolutional neural network,CNN）提取卫星云图和地基云图的特征，实现光伏功率的超短期预测。通过对云层动态变化的准确捕捉，该方法能够有效缓解由自然因素引起的光伏功率波动给电网带来的压力，为推动光伏发电技术的广泛应用和电网的智能化管理提供坚实的技术支持。
Abstract：As one of the richest sources of renewable energy,solar energy holds significant development potential,especially in the current context of China's energy structure adjustment. Its development and utilization is particularly important.Photovoltaic(PV)power generation is one of the most widely employed methods for harnessing solar energy.However,the efficiency and stability of PV power generation are significantly affected by natural factors,such as irradiance levels and variations in cloud cover,which can lead to diurnal fluctuations in PV power.In order to improve the accuracy of PV power prediction,this paper proposes a novel PV power prediction method that combines satellite cloud maps and ground-based cloud maps.The method utilizes a convolutional neural network to extract features from both satellite and ground-based cloud maps,enabling ultra-short-term predictions of PV power output. By accurately capturing the dynamic changes in cloud cover,the method can effectively alleviate the pressure on the power grid caused by PV power fluctuations due to natural factors.Furthermore,it provides robust technical support for the widespread adoption of PV power generation technology and the intelligent management of the power grid.
[1]史丹.中国能源安全结构研究[M].北京：社会科学文献出版社，2015.
[2]刘晓艳，王珏，姚铁锤，等.基于卫星遥感的超短期分布式光伏功率预测[J].电工技术学报，2022,37(7):1800-1809.LIU Xiaoyan,WANG Jue,YAO Tiechui,et al. Ultra short-term distributed photovoltaic power prediction based on satellite remote sensing[J].Transactions of China Electrotechnical Society,2022,37(7):1800-1809.
[3]崔杨，张汇泉，仲悟之，等.考虑需求响应的含光热电站可再生能源高渗透率电力系统多源优化调度[J].高电压技术，2020,46(5):1499-1507.CUI Yang,ZHANG Huiquan,ZHONG Wuzhi,et al.Multi-source optimal scheduling of renewable energy high-permeability power system with CSP plants considering demand response[J]. High Voltage Engineering,2020,46(5):1499-1507.
[4]赖昌伟，黎静华，陈博，等.光伏发电出力预测技术研究综述[J].电工技术学报，2019,34(6):1201-1217.LAI Changwei,LI Jinghua,CHEN Bo,et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society,2019,34(6):1201-1217.
[5]时珉，许可，王珏，等.基于灰色关联分析和GeoMAN模型的光伏发电功率短期预测[J].电工技术学报，2021,36(11):2298-2305.SHI Min,XU Ke,WANG Jue,et al.Short-term photovoltaic power forecast based on grey relational analysis and GeoMAN model[J].Transactions of China Electrotechnical Society,2021,36(11):2298-2305.
[6]董存，王铮，白捷予，等.光伏发电功率超短期预测方法综述[J].高电压技术，2023,49(7):2938-2951.DONG Cun,WANG Zheng,BAI Jieyu,et al.Review of ultra-shortterm forecasting methods for photovoltaic power generation[J].High Voltage Engineering,2023,49(7):2938-2951.
[7]陈志宝，李秋水，程序，等.基于地基云图的光伏功率超短期预测模型[J].电力系统自动化，2013,37(19):20-25.CHEN Zhibao,LI Qiushui,CHENG Xu,et al. A very short-term prediction model for photovoltaic power based on ground-based cloud images[J].Automation of Electric Power Systems,2013,37(19):20-25.
[8]钟振兴，马晓波，安巍，等.基于地基云图与气象数据的辐照度轻量化预测[J].东北电力大学学报，2021,41(1):24-30.ZHONG Zhenxing,MA Xiaobo,AN Wei,et al.A lightweight solar irradiance prediction model based on ground-based cloud images and meteorological data[J]. Journal of Northeast Electric Power University,2021,41(1):24-30.
[9]余光正，陆柳，汤波，等.基于云图特征提取的改进混合神经网络超短期光伏功率预测方法[J].中国电机工程学报，2021,41(20):6989-7003.YU Guangzheng,LU Liu,TANG Bo,et al. An improved hybrid neural network ultra-short-term photovoltaic power forecasting method based on cloud image feature extraction[J].Proceedings of the CSEE,2021,41(20):6989-7003.
[10]朱想，周海，朱婷婷，等.光伏系统中地基云图的预处理[J].电力系统自动化，2018,42(6):140-145.ZHU Xiang,ZHOU Hai,ZHU Tingting,et al. Pre-processing of ground-based cloud images in photovoltaic system[J].Automation of Electric Power Systems,2018,42(6):140-145.
[11]路志英，周庆霞，李鑫，等.基于地基云图图像特征的光伏功率预测[J].电力系统及其自动化学报，2020,32(8):70-76.LU Zhiying,ZHOU Qingxia,LI Xin,et al. Photovoltaic power prediction based on image features of ground cloud image[J].Proceedings of the CSU-EPSA,2020,32(8):70-76.
[12]柴闵康，夏飞，张浩，等.基于云图特征自识别的光伏超短期预测模型[J].电网技术，2021,45(3):1023-1031.CHAI Minkang,XIA Fei,ZHANG Hao,et al. Ultra-short-term prediction of self-identifying photovoltaic based on sky cloud chart[J].Power System Technology,2021,45(3):1023-1031.
[13]SUN Y C,SZ-CS G,BRANDT A R. Solar PV output prediction from video streams using convolutional neural networks[J].Energy&Environmental Science,2018,11(7):1811-1818.
[14]蒋俊霞，高晓清，吕清泉，等.基于地基云图的云跟踪与太阳辐照度超短期预报方法研究[J].太阳能学报，2020,41(5):351-358.JIANG Junxia,GAO Xiaoqing,LYU Qingquan,et al. Study on cloud tracking and solar irradiance ultra-short-term forecasting based on tsi images[J].Acta Energiae Solaris Sinica,2020,41(5):351-358.
[15]ALONSO-MONTESINOS J, BATLLES F J. Solar radiation forecasting in the short-and medium-term under all sky conditions[J].Energy,2015,83:387-393.
[16]CHENG H Y. Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting[J].Renewable Energy,2017,104:281-289.
[17]CHOW C W,BELONGIE S,KLEISSL J.Cloud motion and stability estimation for intra-hour solar forecasting[J].Solar Energy,2015,115:645-655.
[18]CHU Y H,PEDRO H T C,LI M Y,et al.Real-time forecasting of solar irradiance ramps with smart image processing[J]. Solar Energy,2015,114:91-104.
[19]LI M Y,CHU Y H,PEDRO H T C,et al.Quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term DNI forecasts[J].Renewable Energy,2016,86:1362-1371.
[20]MARQUEZ R,PEDRO H T C,COIMBRA C F M. Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs[J].Solar Energy,2013,92:176-188.
[21]KATO T,MANABE Y,FUNABASHI T,et al.A study on several hours ahead forecasting of spatial average irradiance using NWP model and satellite infrared image[C]∥2016 International Conference on Probabilistic Methods Applied to Power Systems(PMAPS).IEEE,2016:1-8.
[22]司志远，杨明，于一潇，等.基于卫星云图特征区域定位的超短期光伏功率预测方法[J].高电压技术，2021,47(4):1214-1223.SI Zhiyuan,YANG Ming,YU Yixiao,et al. Ultra-short-term photovoltaic power prediction method based on satellite image feature region positioning[J].High Voltage Engineering,2021,47(4):1214-1223.
基本信息:
DOI：10.20097/j.cnki.issn1007-9904.240618
中图分类号:TM615;TP183
引用信息:
[1]马文文,胡思雨,杨凡,等.基于地基云图与卫星云图相结合的光伏功率预测方法[J].山东电力技术,2025,52(12):17-26.DOI:10.20097/j.cnki.issn1007-9904.240618.
基金信息:
国家电网公司总部管理科技项目“基于分布式光伏多层级预测的配电网运行风险预控关键技术研究及应用”（5400-202355555A-3-2-ZN）~~
2024-12-31
2024
2025-04-11
2025
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2025-12-25
2025-12-25