Full Text:   <876>

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CLC number: TM715

On-line Access: 2016-02-02

Received: 2015-05-29

Revision Accepted: 2015-09-15

Crosschecked: 2016-01-16

Cited: 1

Clicked: 1834

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yun-luo Yu

http://orcid.org/0000-0003-3717-5296

Wei Li

http://orcid.org/0000-0002-3893-0030

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Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.2 P.101-114

http://doi.org/10.1631/jzus.A1500156


A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network


Author(s):  Yun-luo Yu, Wei Li, De-ren Sheng, Jian-hong Chen

Affiliation(s):  Institute of Thermal Science and Power System, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   energy@zju.edu.cn

Key Words:  Ensemble empirical mode decomposition (EEMD), Intrinsic mode functions (IMFs), Back propagation neural network (BPNN), Short-term load forecasting (STLF)


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.

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author="Yun-luo Yu, Wei Li, De-ren Sheng, Jian-hong Chen",
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pages="101-114",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1500156"
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%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
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%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
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PB - Zhejiang University Press & Springer
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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).

一种基于改进总体经验模态分解与反向传播神经网络的短期负荷预测方法

目的:短期电力负荷预测是电力系统安全调度、经济运行的重要依据。研究处理非线性、非稳态电力负荷信号的新方法,建立短期负荷预测的混合模型,提高短期负荷预测的精确度。
创新点:1. 提出一种改进总体经验模态分解(EEMD)方法,抑制传统EEMD方法中的端点效应问题; 2. 提出一种基于改进EEMD和反向传播神经网络(BPNN)的短期负荷预测方法。
方法:1. 使用改进的EEMD方法将非稳态、非线性的电力负荷信号分解为一系列的内禀模态函数和一个趋势余量;2. 移除所得的高频内禀模态函数;3. 使用BPNN分别预测各内禀模态函数及趋势余量;4. 使用BPNN组合各内禀模态函数及趋势余量预测结果,即为最终负荷预测结果。
结论:1.所提出的改进EEMD方法能有效抑制传统EEMD方法中的端点效应问题;2. 在相同条件下,所提出的基于改进EEMD和BPNN的短期负荷预测方法较 BPNN、EMD-BPNN、EEMD-BPNN、SARIMA-BPNN、WTNNEA和WGMIPSO预测方法有更高的精确度。

关键词:集合经验模态分解;内禀模态函数;反向传播神经网络;短期负荷预测

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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