NDLI: Hybrid of EMD and SVMs for Short-Term Load Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Zhihui Zhu Yunlian Sun Huangqiang Li
Copyright Year	2007
Description	Author affiliation: Wuhan Univ., Wuhan (Zhihui Zhu; Yunlian Sun; Huangqiang Li)
Abstract	The electric power load is inherently nonlinear and nonstationary time series, which has periodicity and randomicity. It is difficult to accurately forecast with a single method. So a hybrid method based on empirical mode decomposition (EMD) and support vector machines (SVMs) was presented in this paper. First, load time series was adaptively decomposed into a series of smooth intrinsic mode functions (IMFs) with different scales via EMD; second, tendency of each IMF was forecasted with different SVM respectively according to its change and weather factors were taken account in forecasting; finally, final results were obtained by summing up these forecasting results of each IMF together. The simulation results show that the hybrid method has higher precision and greater generalization ability.
Starting Page	1044
Ending Page	1047
File Size	1567163
Page Count	4
File Format	PDF
ISBN	9781424408177
DOI	10.1109/ICCA.2007.4376516
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Weather forecasting Artificial neural networks Support vector machines Signal processing Power system modeling Predictive models Automatic control Automation Sun support vector machines load forecasting empirical mode decomposition
Content Type	Text
Resource Type	Article

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