李芬¹(), 周尔畅¹, 孙改平¹, 白永清², 童力³, 刘邦银⁴, 赵晋斌¹

1.上海电力大学 电气工程学院,上海 200090
2.中国气象局武汉暴雨研究所, 武汉 430205
3.国网浙江省电力有限公司电力科学研究院,杭州 310014
4.华中科技大学 强电磁工程与新技术国家重点实验室, 武汉 430074

收稿日期:2021-07-20出版日期:2021-12-28发布日期:2021-12-30

作者简介:李 芬,女,副教授,电话(Tel.):021-35303155;E-mail: lifen2012@shiep.edu.cn.
基金资助:国家自然科学基金面上项目(51777120);上海市绿色能源并网工程技术研究中心(13DZ2251900);国网浙江省电力公司科技项目(5211DS190037)

A Novel Weather Classification Method and Its Application in Photovoltaic Power Prediction

LI Fen¹(), ZHOU Erchang¹, SUN Gaiping¹, BAI Yongqing², TONG Li³, LIU Bangyin⁴, ZHAO Jinbin¹

1. College of Electrical Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. Wuhan Institute of Heavy Rain of China Meteorological Administration, Wuhan 430205, China
3. Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China
4. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2021-07-20Online:2021-12-28Published:2021-12-30

摘要/Abstract

摘要： 为提高光伏功率预测准确率提出了一种新的天气分型方法,该方法首先按总云量大小区分晴天和云天,然后根据太阳被遮蔽的程度将云天进一步细分为三类.该方法能有效识别影响光伏出力的关键气象环境因子特征,并对其加权求和得到新型分类指标Sky Condition Factor(SCF).该方法物理意义明确,区分度较好且易于量化.对天气类型合理细分后,可消除众多气象环境因子之间的耦合关系,降低输入变量维度,易于统计建模.最后分别基于原理和统计方法进行建模验证,结果显示该方法可以有效提高光伏功率预测的准确率.
关键词: 光伏功率预测, 天气分型, 气象环境因子, 原理预测法, 统计预测法
Abstract: To improve the accuracy of photovoltaic (PV) power prediction, this paper proposes a novel weather classification method. First, it distinguishs the clear days and cloudy days according to the total cloud cover. Then, it further classifies the cloudy days into three subtypes to investigate whether the sun is obscured by clouds. This method can effectively identify the characteristics of key meteorological environmental factors that affect PV output and form a new classification index sky condition factor (SCF) by weighted summation. This method has clear physical meanings, good discrimination, and easy quantification. The reasonable classification of weather types can eliminate the coupling relationship between many meteorological environmental factors and reduce the dimension of input variables, which makes it easy for statistical modeling. Based on the theoretical and the statistical approachs respectively, the modeling and verification are conducted and the results show that the method can effectively improve the accuracy of PV power prediction.
Key words: photovoltaic (PV) power prediction, weather type classification, meteorological environmental factors, physical approach, statistic approach


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