Short-term Load Forecasting based on Wavelet-Particle Swarm

被引：0
|
作者：
Yin Xin [1 ]
Zhou Ye [1 ]
He Yi-gang [1 ]
Zhu Jun-fei [2 ]
机构：
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
[2] Hunan Elect Power Co, Dispatch Commun Dept, Changsha 410001, Peoples R China
来源：
2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC) | 2010年
关键词：
wavelet packet analysis; short-term power load forecasting; hourly temperature factor; particle swarm optimization; neural network;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In view of the power load with the randomicity and the complexity, the short-term power load forecasting based on optimal wavelet-particle swarm is introduced in this paper. First, the power load series is decomposed several frequency ranges by wavelet packet. Select the optimal wavelet tree to reconstruct the coefficients of the wavelet packet and form the number of power load components. Then, forecast the reconstructed series with the particle swarm optimization neural network, respectively, introduce hourly temperature factor for the low frequency components and promote the prediction precision by the newest temperature information. In addition, taken a city's power system into test and simulation to test the advantage of this method, and proved that it has more advantage and better efficiency.
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页数：4
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