基于互补型集成经验模态分解模糊熵和回声状态网络的短期电力负荷预测

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3651-3655.

基于互补型集成经验模态分解模糊熵和回声状态网络的短期电力负荷预测

李青¹,李军¹,马昊²

1. 兰州交通大学自动化与电气工程学院,兰州 730070;
2. 宁夏东部热电股份有限公司, 银川 750000

收稿日期:2014-05-26 修回日期:2014-07-01 出版日期:2014-12-01 发布日期:2014-12-31
通讯作者: 李青
作者简介:李青(1989-),男(回族),甘肃天水人,硕士研究生, 主要研究方向:计算智能、电力负荷预测;李军(1969－),男(回族), 甘肃兰州人,教授,博士, 主要研究方向:计算智能、复杂非线性系统的建模、预测与控制；马昊(1988－),男(回族), 甘肃天水人,助理工程师, 主要研究方向:计算智能、风电功率预测。
基金资助:
甘肃省财政厅基本业务费资助项目;甘肃省教育厅硕导项目

Short-term electricity load forecasting based on complementary ensemble empirical mode decomposition-fuzzy permutation and echo state network

LI Qing¹,LI Jun¹,MA Hao²

1. College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Gansu Lanzhou 730070,China;
2. East Thermal Power Company Limited, Yinchuan Ningxia 750000,China

Received:2014-05-26 Revised:2014-07-01 Online:2014-12-01 Published:2014-12-31
Contact: LI Qing

摘要/Abstract

摘要：

为了提高短期电力负荷预测的精度,提出一种噪声互补型集成经验模态分解(CEEMD)模糊熵和泄漏积分型ESN(LiESN)的组合预测方法。为降低对负荷序列进行局部分析的计算规模以及提高负荷预测的准确性,首先采用CEEMD模糊熵将负荷时间序列分解为具有明显复杂度差异的负荷子序列；然后,通过对各子序列进行特性分析,分别构建相应的子LiESN预测模型；最后将各子序列的预测结果叠加得到最终预测值。将CEEMD模糊熵结合LiESN的组合预测方法应用于美国新英格兰地区短期电力负荷实例中,仿真结果表明,所提出的组合预测方法具有很高的预测精度。

Abstract:

Based on Complementary Ensemble Empirical Mode Decomposition (CEEMD)-fuzzy entropy and Echo State Network (ESN) with Leaky integrator neurons (LiESN), a kind of combined forecast method was proposed for improving the precision of short-term power load forecasting. Firstly, in order to reduce the calculation scale of partial analysis for power load series and improve the accuracy of load forecasting, the power load time series was decomposed into a series of power load subsequences with obvious differences in complex degree by using CEEMD-fuzzy entropy, according to the characteristics of each subsequence, and then the corresponding LiESN forecasting submodels were built, the ultimate forecasting results could be obtained by the superposition of the forecasting model. The CEEMD-LiESN method was applied to the instance of short term electricity load forecasting of the New England region. The experimental results show that the proposed combination forecasting method has a high prediction precision.

中图分类号:

TP391.9

李青李军马昊. 基于互补型集成经验模态分解模糊熵和回声状态网络的短期电力负荷预测[J]. 计算机应用, 2014, 34(12): 3651-3655.

LI Qing LI Jun MA Hao. Short-term electricity load forecasting based on complementary ensemble empirical mode decomposition-fuzzy permutation and echo state network[J]. Journal of Computer Applications, 2014, 34(12): 3651-3655.

参考文献

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