基于局域波的地区负荷分析及其短期负荷预测研究
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摘要
本文提出基于局域波的地区负荷分析及其短期负荷预测新方法。首先利用局域波和近似熵理论深入分析地区负荷特性,将负荷序列局域波分解,得到能反映负荷组成的分量及余量;对分量进行Hilbert变换得到Hilbert时频谱和功率谱,从而分析出各分量对总负荷的波动贡献;再将近似熵作为各分量及余量的特征参数,对比各分量和实际不同类型负荷的近似熵值,研究各分量的物理含义;采用实时气象因素对各负荷分量每天不同时段进行精细化分析,挖掘不同因素对各分量的影响状况。研究了电气化铁路的负荷特性及影响因素,提出基于灾变遗传算法和时序的LS-SVM的预测新方法,该法以时间序列模型与影响因素分析为据确定输入变量,利用灾变遗传优化算法得到模型的最优参数,建立LS-SVM预测模型。最后选择适合各负荷分量及余量的预测模型,然后进行合理重构,得出最后负荷预测结果。
This paper presents area load analysis and short-term load forecasting based on Local Wave. Firstly, load characteristics are analyzed by using Local Wave and ApEn. Load is decomposed by local wave. Components reflecting load composition are obtained. Secondly, components are carried on Hilbert transformation obtaining Hilbert time-frequency and power spectrum. Next, ApEn is taken as characteristic parameter, then a comparison is made between components and actual load to learn the components’physical meaning. Then, real-time climatic factor is used to analyze load components of different time everyday, and influence of factors can be evaluated. Others, a forecasting method based on Cataclysmic GA and time-series LS-SVM is proposed. LS-SVM input variables are determined by influencing factor analysis and time-series model. It optimizes parameters using Cataclysmic GA, and establishes LS-SVM model. Finally, appropriate forecasting model for components and remainder are selected and reconstructed, and acquire final forecasting results.
This paper presents area load analysis and short-term load forecasting based on Local Wave. Firstly, load characteristics are analyzed by using Local Wave and ApEn. Load is decomposed by local wave. Components reflecting load composition are obtained. Secondly, components are carried on Hilbert transformation obtaining Hilbert time-frequency and power spectrum. Next, ApEn is taken as characteristic parameter, then a comparison is made between components and actual load to learn the components’physical meaning. Then, real-time climatic factor is used to analyze load components of different time everyday, and influence of factors can be evaluated. Others, a forecasting method based on Cataclysmic GA and time-series LS-SVM is proposed. LS-SVM input variables are determined by influencing factor analysis and time-series model. It optimizes parameters using Cataclysmic GA, and establishes LS-SVM model. Finally, appropriate forecasting model for components and remainder are selected and reconstructed, and acquire final forecasting results.
引文
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[3]马建伟,张国立.人工鱼群神经网络在电力系统短期负荷预测中的应用.电网技术,2005,29(11):37-39
[4]谢开贵,李春燕,周家启.基于神经网络的负荷组合预测模型研究.中国电机工程学报,2002,22(7):86-89
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[6]张智晟,孙雅明,王兆峰,等.优化相空间邻近点与递归神经网络融合的短期负荷预测.中国电机工程学报,2003,23(8):45-49
[7]谢宏,程浩忠,张国立,等.基于粗糙集理论建立短期电力负荷神经网络预测模型.中国电机工程学报,2003,23(11):2-4
[8]吴宏晓,侯志俭,刘涌,等.基于免疫聚类径向基函数网络模型的短期负荷预测.中国电机工程学报,2005,25(16):54-56
[9]尤勇,盛万兴,王孙安.基于人工免疫网络的短期负荷预测模型.中国电机工程学报,2003,23(3):27-29,98
[10]李元诚,方廷健,于尔铿.短期负荷预测的支持向量机方法研究.中国电机工程学报,2003,23(6):56-59
[11]王锡淮,朱思锋.基于支持向量机的船舶电力负荷预测.中国电机工程学报2004,24(10):37-39
[12]赵登福,庞文晨,张讲杜,等.基于贝叶斯理论和在线学习支持向量机的短期负荷预测.中国电机工程学报,2005,25(13):9-13
[13]牛东晓,刘达,陈广娟,等.基于遗传优化的支持向量机小时负荷滚动预测.电工技术学报2007,22(6):149-153
[14]谢宏,魏江平,刘鹤立.短期负荷预测中支持向量机模型的参数选取和优化方法.中国电机工程学报,2006,26(22):18-22
[15] Zongwu Cai, Ram C.Tiwari. Application of a local linear autoregressive model to BOD time series. Environmentrics,2000,11(3): 341-450
[16] Yuehui Chen , Bo Yang, Jiwen Dong. Time-series prediction using a locallinear wavelet net work. Neurocompputing, 2006,69: 449-465
[17]张凯,姚建刚,李伟,等.基于功率谱分解和实时气象因素的短期负荷预测.电网技术,2007,31(23):48-51
[18]谢宏,陈志业,牛东晓,等.基于小波分解与气象因素影响的电力系统日负荷预测模型研究.中国电机工程学报,2001,21(5):6-10
[19]苏娟,杜松怀,李才华.基于多因素小波分析的神经网络短期现货电价预测方法.电力自动化设备,2007,27(11):27-33
[20]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测.电力系统自动化,2007,31(9):37-41
[21]祝志慧,孙云莲,季宇.基于EMD和SVM的短期负荷预测.高电压技术,2007,33(5):118-122
[22]黎静华,栗然,顾雪平,等.网格化的电力系统短期负荷预测的MDRBR模型.电力系统自动化,2005,29(24):27-31
[23]栗然,刘宇,黎静华,等.基于改进决策树算法的日特征负荷预测研究.中国电机工程学报,2005,25(23):37-41
[24] L. Cao. Support vector machines experts for time series forecasting.Neurocomputing,2003,51: 321-339
[25] Huang N E,Shen Z, Long S R ,et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. London A,1998,454: 903-995
[26]马孝江,余伯,张志新.一种新的时频分析方法——局域波法.振动工程学报,2000,13(9):24-29
[27] Melvile W K.Wave modulation and breakdown.Journal of Fluid Mech.1983,128: 489-506
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