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电网技术  2006 

基于支持向量机的中长期日负荷曲线预测

, PP. 56-60

Keywords: 中长期负荷预测,日负荷曲线,支持向量机

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Abstract:

提出了一种预测中长期日负荷曲线的新方法,通过历史典型日负荷数据构造出典型日年度发展时间序列,运用支持向量机方法对预测日各时刻负荷值进行预测并得到了典型日负荷曲线。该方法不需要对日负荷特性、最大负荷及需电量进行预测,因此避免了可能的误差积累问题。以某电网为例对该方法进行了测试,结果表明其具有较高的预测精度。

References

[1]  张伏生,刘芳,赵文彬,等.灰色Verhulst模型在中长期负荷预测中的应用[J].电网技术,2003,27(5):37-39.
[2]  Zhang Fusheng,Liu Fang,Zhao Wenbin,et al.Application of grey verhulst model in middle and long term load forecasting[J].Power System Technology,2003,27(5):37-39(in Chinese).
[3]  余健明,燕飞,杨文宇,等.中长期电力负荷的变权灰色组合预测模型[J].电网技术,2005,29(17):26-29.
[4]  Zhao Dengfu,Wang Meng,Zhang Jiangshe,et al.A support vector machine approach for short term load forecasting[J].Proceedings of the CSEE,2002,22(4):26-30(in Chinese).
[5]  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[J].中国电机工程学报.2003,23(6):55-59.
[6]  Li Yuancheng,Fang Tingjian,Yu Erkeng.Study of support vector machines for short-term load forecasting[J].Proceedings of the CSEE,2003,23(6):55-59(in Chinese).
[7]  Yu Jianming,Yan Fei,Yang Wenyu,et al.Gray variable weight combination model for middle and long term load forecasting [J].Power System Technology,2005,29(17):26-29(in Chinese).
[8]  罗治强,张焰,朱杰.粗糙集理论在电力系统负荷预测中的应用[J].电网技术,2004,28(3):29-32.
[9]  Luo Zhiqiang,Zhang Yan,Zhu Jie.Application of rough set theory in electric power load forecast[J].Power System Technology,2004,28(3):29-32(in Chinese).
[10]  赵儆,康重庆,葛睿,等.电力市场中多日负荷曲线的预测[J].电力系统自动化设备,2002,22(9):31-33.
[11]  Zhao Jing,Kang Chongqing,Ge Rui,et al.Multi-day load curve forecasting in electricity market[J].Electric Power Automation Equipment,2002,22(9):31-33(in Chinese).
[12]  康重庆,夏清,相年德,等.中长期日负荷曲线预测的研究[J].电力系统自动化,1996,20(6):16-20.
[13]  Kang Chongqing,Xia Qing,Xiang Niande,et al.The study on long-term daily load curve forecasting[J].Automation of Electric Power Systems,1996,20(6):16-20(in Chinese).
[14]  何光宇,郭家春,鲍毅,等.一种中长期日负荷曲线预测的新方法——双向夹逼法[J].电网技术,2004,28(7):27-29.
[15]  He Guangyu,Guo Jiachun,Bao Yi,et al.A novel method of middle and long term forecasting for daily load curve?approaching algorithm two directions[J].Power System Technology,2004,28(7):27-29(in Chinese).
[16]  赵晖.用样条插值法模拟典型日负荷曲线[J].电网技术,1998,22(5):39-41.
[17]  Zhao Hui.Simulation of typical daily load curve with spline interpolation[J].Power System Technology,1998,22(5):39-41(in Chinese).
[18]  赖敏.日负荷曲线的二次函数修正预测[J].华中电力,2004,17(2):5-7.
[19]  Lai Min.The method of the quadratic revision functions for daily load forecasting[J].Central China Electric Power,2004,17(2):5-7(in Chinese).
[20]  李扬,王治华,卢毅,等.南京市夏季气温-日峰荷特性分析[J].电网技术,2001,25(7):63-66.
[21]  Li Yang,Wang Zhihua,Lu Yi,et al.Characteristic analysis of summer air temperature?daily peak load in Nanjing[J].Power System Technology,2001,25(7):63-66(in Chinese).
[22]  N.Vapnik.The nature of statistical learning theory[M].New York:Springer-Verlag,1995.
[23]  赵登福,王蒙,张讲社,等.基于支撑向量机方法的短期负荷预测[J].中国电机工程学报,2002,22(4):26-30.
[24]  杨廷西,刘丁.基于小波变换和最小二乘支持向量机的短期电力负荷预测[J].电网技术,2005,29(13):60-64.
[25]  Yang Yanxi,Liu Ding.Short-term load forecasting based on wavelet transform and least square support vector machines[J].Power System Technology,2005,29(13):60-64(in Chinese).
[26]  赵登福,庞文晨,张讲社,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[J].中国电机工程学报,2005,25(13):8-13.
[27]  Zhao Dengfu,Pang Wenchen,Zhang Jiangshe,et al.Based on Bayesian theory and online learning SVM for short term load forecasting[J].Proceedings of the CSEE,2005,25(13):8-13(in Chinese).
[28]  Smola A,Sch?lkopf B.A tutorial on support vector regression [R].London:Royal Holloway Coll., Univ.,1998.
[29]  Chang C C,Lin C J.LIBSVM:A library for support vector machines[DB/OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm,2001.
[30]  Chen Bo-Juen,Chang Ming-Wei,Li Chih-Jen.Load forecasting using support vector machines:a study on EUNITE competition 2001 [J].IEEE Trans. on Power Systems,2004,19(4):1821-1830.

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