基于小波软阈值的中长期电力负荷预测研究被引量：1

Study on Mid-and Long-term Power Load Forecasting Based on Wavelet Soft-threshold Technology

下载PDF

导出

摘要由于中长期电力系统负荷数据相对较少,影响因素广泛,常用的负荷预测方法很难达到满意的精度,所以提出一种结合小波软阈值和广义回归神经网络的综合预测模型,该模型充分考虑了长期负荷数据相对较少和影响因素广泛的特点,把负荷预测看作一个信号序列,应用小波去噪原理,将信号与噪声分离,然后利用广义回归神经网络进行预测。实际应用证明,预测结果是令人满意的。 Mid-and long-term load data of power system is comparatively inadequate and influenced by many factors. Hence it is difficult to achieve satisfactory precision by use of common load forecast methods. This paper presents a comprehensive forecast model integrating wavelet soft-threshold with general regression neural network （GRNN）. With full consideration of the characteristics of long-term load data, the load forecast is viewed as a signal sequence and the signal is denoised with wavelet denoising theory. Then GRNN is used to forecast the load. Practical applications show that the forecast precision is satisfying.

作者吴耀华

机构地区陕西理工学院电气工程系

出处《广东电力》 2007年第12期5-8,29,共5页 Guangdong Electric Power

关键词广义回归神经网络中长期负荷预测小波软阈值小波去噪原理 general regression neural network CGRNN） mid- and long-term load forecast wavelet soft-threshold waveletdenoising theory

分类号 TM715 [电气工程—电力系统及自动化]

引文网络
相关文献

参考文献3

1杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1998..
2冯志鹏,宋希庚,薛冬新,郑爱萍,孙玉明.基于广义回归神经网络的时间序列预测研究[J].振动．测试与诊断,2003,23(2):105-109. 被引量：48
3谷志红,牛东晓,王会青.广义回归神经网络模型在短期电力负荷预测中的应用研究[J].中国电力,2006,39(4):11-14. 被引量：32

二级参考文献14

1张雪莹,管霖,谢锦标.采用谱分析建模和基于人工神经网络的短期负荷预测方案[J].电网技术,2004,28(11):49-52. 被引量：8
2胡上序.观测数据的分析与处理[M].杭州：浙江大学出版社,2002..
3Specht D F. A general regression neural network. IEEE Transactions on Neural Networks, 1991,2(6) :568~576.
4Tomandl D, Schober A. A modified general regression neuralnetwork (MGRNN) with new, efficient trainingalgorithms as a robust 'black box'-tool for data analysis. Neural Networks,2001,14(4) : 1023~1034.
5Chtioui Y, Panigrahi S,Francl L. A generalized regression neural network and its application for leaf wentness prediction to forecast plant disease. Chemometrics and Intelligent Laboratory Systems, 1999,48 (1) : 47~58.
6Leung M T,Chen A S,Daouk H. Forecasting exchange rates using general regression neural networks. Computers & Operation Research,2000,27(4) : 1093~1110.
7Specht D F,Romsdahl H. Experience with adaptive probabilistic neural networks and adaptive general regression neural networks, In:Proceedings of the IEEE World Congress on Computational Intelligence, 1994,2:1203~1208.
8RUTKOWSKI L. Generalized regression neural network in time-varyingenvironment [J ], IEEE Transaction on Neural Network, 2004,15 (3):576-596.
9TOMANDL D, SCHOBER A. A modified general regression neural network with new, efficient training algorithms as a robust ‘black box'tool for data analysis [ J ]. Neural Networks, 2001,14 (8): 1023-1034.
10CHENG Shih-lian, HWANG Chyi. Optimal approximation of linear systems by a differential evolution algorithm [ J ]. IEEE Transaction on Systems, Man and Cybernetics, 2001, 31 (6):698-707.