CLC number: TM715
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-01-16
Cited: 1
Clicked: 5949
Citations: Bibtex RefMan EndNote GB/T7714
Yun-luo Yu, Wei Li, De-ren Sheng, Jian-hong Chen. A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network[J]. Journal of Zhejiang University Science A, 2016, 17(2): 101-114.
@article{title="A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network",
author="Yun-luo Yu, Wei Li, De-ren Sheng, Jian-hong Chen",
journal="Journal of Zhejiang University Science A",
volume="17",
number="2",
pages="101-114",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1500156"
}
%0 Journal Article
%T A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network
%A Yun-luo Yu
%A Wei Li
%A De-ren Sheng
%A Jian-hong Chen
%J Journal of Zhejiang University SCIENCE A
%V 17
%N 2
%P 101-114
%@ 1673-565X
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500156
TY - JOUR
T1 - A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network
A1 - Yun-luo Yu
A1 - Wei Li
A1 - De-ren Sheng
A1 - Jian-hong Chen
J0 - Journal of Zhejiang University Science A
VL - 17
IS - 2
SP - 101
EP - 114
%@ 1673-565X
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1500156
Abstract: short-term load forecasting (STLF) plays a very important role in improving the economy and security of electricity system operations. In this paper, a hybrid STLF method is proposed based on the improved ensemble empirical mode decomposition (IEEMD) and back propagation neural network (BPNN). To alleviate the mode mixing and end-effect problems in traditional empirical mode decomposition (EMD), an IEEMD is presented based on the degree of wave similarity. By applying the IEEMD method, the nonlinear and nonstationary original load series is decomposed into a finite number of stationary intrinsic mode functions (IMFs) and a residual. Among these components, the high frequency (namely IMF1) is always so small that it has little contribution to model fitting, while it sometimes has a great disturbance for the STLF. Therefore, the IMF1 is removed in the proposed hybrid method for denoising. The remaining IMFs and residual are forecast by BPNN, and then the forecasting results of each component are combined with BPNN to obtain the final predicted load series. Three groups of studies were done to evaluate the effectiveness of the proposed hybrid method. The results show that the proposed hybrid method outperforms other methods both mentioned in this paper and previous studies in terms of all the three standard statistical indicators considered in this study.
Authors propose a hybrid Short-term load forecasting (STLF) method based on the Improved Ensemble Empirical Mode Decomposition (IEEMD) and Back Propagation Neural Network (BPNN).
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