Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

被引:55
作者
Hong, Wei-Chiang [1 ]
Fan, Guo-Feng [2 ]
机构
[1] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
[2] Ping Ding Shan Univ, Sch Math & Stat, Ping Ding Shan 467000, Henan, Peoples R China
来源
ENERGIES | 2019年 / 12卷 / 06期
关键词
empirical mode decomposition (EMD); particle swarm optimization (PSO) algorithm; intrinsic mode function (IMF); support vector regression (SVR); short term load forecasting; FUZZY TIME-SERIES; NEURAL-NETWORKS; ELECTRICITY CONSUMPTION; INTELLIGENT ALGORITHM; ENERGY-CONSUMPTION; SVR; OPTIMIZATION; EVOLUTIONARY; SELECTION;
D O I
10.3390/en12061093
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
For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.
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页数:16
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