A Hybrid Method for Short-term Load Forecasting in Power System

被引：0
作者：
Zhu, Xianghe [1 ]
Qi, Huan [1 ]
Huang, Xuncheng [2 ]
Sun, Suqin [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Wuchang Branch, Dept Basic Sci, Wuhan 430064, Peoples R China
[2] Elect Power HeNan, Zhengzhou 450052, Peoples R China
来源：
PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年
关键词：
hybrid method; ensemble empirical mode decomposition (EEMD); least square-support vector machine (LS-SVM); BP neural network; short-term load forecasting;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to improve the accuracy of power load forecasting, this paper proposes a hybrid model based on Ensemble Empirical Mode Decomposition (EEMD), least square-support vector machine (SVM) and BP nature network as a short-term load forecasting model. At first, the actual power load series is decomposed into different new series based on EEMD. Then the right parameters and kernel functions are chosen to build different LS-SVM model respectively, to forecast each intrinsic mode functions, due to the change regulation of each of all resulted intrinsic mode functions. Finally, we use the BP network to reconstruct the forecasted signals of the components and obtain the ultimate forecasting results. Simulation results show that the proposed forecasting method possesses accuracy.
引用
收藏
页码：696 / 699
页数：4
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