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河海大学能源与电气学院,江苏省南京市 211100
Received:09 July 2020,
Revised:2021-01-04,
Published Online:01 March 2021,
Published:10 June 2021
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臧海祥,程礼临,刘玲等.基于数据驱动的太阳辐射估计和预测研究与展望[J].电力系统自动化,2021,45(11):170-183.
ZANG Haixiang,CHENG Lilin,LIU Ling,et al.Research and Prospect for Data-driven Estimation and Prediction of Solar Radiation[J].Automation of Electric Power Systems,2021,45(11):170-183.
臧海祥,程礼临,刘玲等.基于数据驱动的太阳辐射估计和预测研究与展望[J].电力系统自动化,2021,45(11):170-183. DOI: 10.7500/AEPS20200709004.
ZANG Haixiang,CHENG Lilin,LIU Ling,et al.Research and Prospect for Data-driven Estimation and Prediction of Solar Radiation[J].Automation of Electric Power Systems,2021,45(11):170-183. DOI: 10.7500/AEPS20200709004.
准确可靠的太阳辐射估计和预测信息对指导光伏电站规划、光伏发电功率预测等具有重要意义。数据驱动方法能够有效学习长期历史辐射数据特性且不涉及辐射机理的复杂公式分析,是当前太阳辐射估计和预测的主流方法之一。针对基于数据驱动的太阳辐射估计和预测研究,从其原理和关键技术方面进行比较论述,具体包括估计和预测模型的输入、主流数据驱动模型以及模型的精度提升方法,并针对当前太阳辐射估计和预测方法的研究现状及不足进行总结与展望,以更好地配合光伏发电技术领域内的研究和应用。
Accurate and reliable estimation and prediction information of solar radiation is significant to guide the photovoltaic (PV) station planning and PV power generation forecasting. The data-driven method can efficiently learn the characteristics of long-term historical radiation data without involving the complex formula analysis of radiation mechanism
which has become one of the most popular methods for the estimation and prediction of solar radiation. Focusing on the studies on data-driven estimation and prediction of solar radiation
this paper compares and reviews their principles and key issues
including the inputs of estimation and prediction models
major data-driven models and model accuracy improvement methods. The conclusion and prospect are drawn according to the current studies and their drawbacks in the estimation and prediction of solar radiation
so as to better serve the studies and applications in the field of PV power generation.
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