董明, 李晓枫, 杨章, 常益, 任明, 张崇兴, 焦在滨. 基于数据驱动的分布式光伏发电功率预测方法研究进展[J]. 电网与清洁能源, 2024, 40(1): 8-17,28.
引用本文: 董明, 李晓枫, 杨章, 常益, 任明, 张崇兴, 焦在滨. 基于数据驱动的分布式光伏发电功率预测方法研究进展[J]. 电网与清洁能源, 2024, 40(1): 8-17,28.
DONG Ming, LI Xiaofeng, YANG Zhang, CHANG Yi, REN Ming, ZHANG Chongxing, JIAO Zaibin. Research Progress on Data-Driven Prediction Methods for Distributed Photovoltaic Power Generation[J]. Power system and Clean Energy, 2024, 40(1): 8-17,28.
Citation: DONG Ming, LI Xiaofeng, YANG Zhang, CHANG Yi, REN Ming, ZHANG Chongxing, JIAO Zaibin. Research Progress on Data-Driven Prediction Methods for Distributed Photovoltaic Power Generation[J]. Power system and Clean Energy, 2024, 40(1): 8-17,28.

基于数据驱动的分布式光伏发电功率预测方法研究进展

Research Progress on Data-Driven Prediction Methods for Distributed Photovoltaic Power Generation

  • 摘要: 从综述的角度,以分布式光伏系统为对象,分析了功率预测技术的发展情况、存在的难点以及主要影响因素,梳理了应用数据驱动方法实现功率准确预测的技术路线。再以空间相关性、历史出力功率以及气象等影响因素为切入点,梳理了光伏系统数据驱动的功率预测研究现状,分析其相应的数据增强、时空图信息以及特征融合的手段,讨论了技术的优缺点。最后给出了功率预测数据驱动方法研究方向和发展建议。

     

    Abstract: From the perspective of overview,this paper examines the development status, existing difficulties, and main influencing factors of power prediction technology in distributed photovoltaic systems,and outlines the technical route for applying data-driven methods to achieve accurate power prediction. Starting from factors such as spatial correlation,historical output power,and meteorological factors,this paper reviews the current research status of data-driven power prediction in photovoltaic systems, analyzes the corresponding data enhancement, spatio-temporal map information,and feature fusion methods,and discusses the advantages and disadvantages of the technology. Finally,research directions and development suggestions for data-driven methods for power prediction are given.

     

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