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THE VARIABILITY CHARACTERISTICS AND PREDICTION OF GUANGDONG POWER LOAD DURING 2002 – 2004
引用本文:罗森波,纪忠萍,马煜华,骆晓明,曾沁,林少冰.THE VARIABILITY CHARACTERISTICS AND PREDICTION OF GUANGDONG POWER LOAD DURING 2002 – 2004[J].热带气象学报(英文版),2007(2).
作者姓名:罗森波  纪忠萍  马煜华  骆晓明  曾沁  林少冰
作者单位:Guangzhou central meteorological observatory,Guangzhou central meteorological observatory,Guangdong Electric Power Dispatch Center,Guangdong Electric Power Dispatch Center,Guangzhou central meteorological observatory,Guangzhou central meteorological observatory Guangzhou 510080 China,Guangzhou 510080 China,Guangzhou 510060 China,Guangzhou 510060 China,Guangzhou 510080 China,Guangzhou 510080 China
基金项目:Platform for Meteorological Prediction of Power Load in Guangdong Province
摘    要:The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are established using optimization subset regression. The results show that a linear increasing trend is very significant and seasonal change is obvious. The power load exhibits significant quasi-weekly (5 – 7 days) oscillation, quasi-by-weekly (10 – 20 days) oscillation and intraseasonal (30 – 60 days) oscillation. These oscillations are caused by atmospheric low frequency oscillation and public holidays. The variation of Guangdong daily power load is obviously in decrease on Sundays, shaping like a funnel during Chinese New Year in particular. The minimum is found at the first and second day and the power load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong power load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The power load also has relationship with other meteorological elements to some extent during different seasons. The maximum of power load in summer, minimum during Chinese New Year and variation during Labor Day and National Day are well fitted and predicted using the equation established by optimization subset regression and accounting for the effect of workdays and holidays.

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