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

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

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作者：Yun-luo Yu ; Wei Li ; De-ren Sheng…
关键词：Ensemble empirical mode decomposition (EEMD) ; Intrinsic mode functions (IMFs) ; Back propagation neural network (BPNN) ; Short ; term load forecasting (STLF) ; 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

TM715

刊名：Journal of Zhejiang University - Science A
出版年：2016
出版时间：February 2016
年：2016
卷：17
期：2
页码：101-114
全文大小：887 KB
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作者单位：Yun-luo Yu (1)
Wei Li (1)
De-ren Sheng (1)
Jian-hong Chen (1)

1. Institute of Thermal Science and Power System, Zhejiang University, Hangzhou, 310027, China
刊物类别：Engineering
刊物主题：Physics
Mechanics, Fluids and Thermodynamics
Chinese Library of Science
出版者：Zhejiang University Press, co-published with Springer
ISSN：1862-1775

文摘

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. Keywords Ensemble empirical mode decomposition (EEMD) Intrinsic mode functions (IMFs) Back propagation neural network (BPNN) Short-term load forecasting (STLF)

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