Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (12): 1510-1519.doi: 10.16183/j.cnki.jsjtu.2021.264

Special Issue: 《上海交通大学学报》2021年“电气工程”专题 《上海交通大学学报》2021年12期专题汇总专辑

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

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

CLC Number: