摘要
利用2002～2004年广东逐日电力负荷资料,采用小波分析、相关分析等方法研究了广东电力负荷的变化特征及与气象因子的关系,并采用最优子集回归方法建立了预测方程。结果表明,广东电力负荷具有非常明显的线性增长趋势,季节变化明显。存在明显的5～7天的准单周振荡,10～20天的准双周振荡及30～60天左右的季节内振荡。它们主要由大气低频振荡及节假日的影响所致。广东电力负荷在周日具有较明显的下降,春节期间呈明显的漏斗状分布,“五一”、“国庆”长假期间最低值主要出现在1～2日,3日以后逐渐恢复到正常状态。与我国其它地区一样,广东电力负荷对温度的变化最敏感,温度是其主要的影响因子,在不同的季节与不同的气象因子还有一定的关系。用最优子集回归方法建立的回归方程并考虑工作日、节假日期间的影响,对夏季峰值、春节谷值、“五一”、“国庆”期间的变化均有较好的拟合与预测。
The variability characteristics of Guangdong dally electrical 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 the linear increasing trend is very significant and seasonal change is obvious. The electrical load exhibits significant quasi-week （5-7 days） oscillation, quasi-two weeks （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 electrical 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 electrical load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong electrical load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The electrical load also has relationship with other meteorological elements to some extent during different seasons. The maximum of electrical 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.
出处
《热带气象学报》
CSCD
北大核心
2007年第2期153-161,共9页
Journal of Tropical Meteorology
基金
广东省气象局"广东省电力负荷气象预测平台"课题资助
关键词
广东电力负荷
低频振荡
小波分析
最优子集回归
Guangdong electrical load
low frequency oscillation
wavelet analysis
optimization subset regression
