基于HHT的电力系统短期负荷预测模型
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摘要
电力系统短期负荷预测是实现电力系统优化运行的基础,对于电力系统运行的安全性、可靠性和经济性都有显著影响。因此,寻求有效的负荷预测方法以提高预测精度具有重要现实意义。迄今为止,研究人员已经提出了许多有效的预测方法,但仍存在大量改进空间。本文在应用HHT(Nilbert-Huang变换)对负荷进分解的基础上,结合目前较为流行的神经网络、支持向量机、粒子群优化等方法对电力系统短期负荷进行预报,主要进行了以下工作：
通过对电力系统短期负荷数据进行预处理,采用莱特准则剔除异常值并进行小波去噪。在应用HHT对负荷数据进行分解的过程中采用差分算子和累计求和方法对模态混叠现象进行改善,从而更好的实现各频带的分离。通过对分解后得到的一系列频率从高到低的单分量信号IMF,将低频信号进行重构。然后根据各IMF的频率特性选取合适的预测模型。最后将各分量预测的结果相加就得到了最终预测值。对于高频随机分量IMF1,由于其波动性大,本文考虑温度及节假日因素,并采用神经网络和粒子群优化(PSO)方法对其进行组合预测,仿真结果表明该方案达到较好的预测效果。
论文最后以2006年四川省某地电力系统实际负荷数据为样本集进行建模和预测,并以相对误差为性能指标进行了检验。结果表明,该方法达到较高的预测精度。
Power system short-term load forecast is the basis of power system optimization running. It can affect safety, reliability and economy of power system operation. Thus, to find effective method has great importance for enhancing the prediction precision. Researchers have proposed many effective methods, but there is a lot of room for improvement. This paper use HHT to decompose the load datafirstly, then combine some popular methods,such as neural network、Support Vector Maehine、PSO to forecast the power system short-term load. The main work included:
Pretreatment on the load data, applying wright criteria to remove outliers and wavelet algorithm to denoise; use a methed to improve its inherent flaw modal-mixing. A series of IMFs with frequency from high to low can be obtained after decompose the load data, reconstruct the low-frequency signals, then according to each IMF's frequency feature we can chose appropriate model to forecast it. At last we can get the final forecast by add the predicted results of each IMF component. Due to IMF1 has large fluctuation, take temperature and weekday into consideration, otherwise neural network and PSO are used to optimize the combination weights. Simulation results indicate that this method has higher accuracy.
Finally, the actual data of power system load in a certain place in Sichuan Province in 2006 is used as the sample to set prediction model、test results and verify the accuracy of the model.
通过对电力系统短期负荷数据进行预处理,采用莱特准则剔除异常值并进行小波去噪。在应用HHT对负荷数据进行分解的过程中采用差分算子和累计求和方法对模态混叠现象进行改善,从而更好的实现各频带的分离。通过对分解后得到的一系列频率从高到低的单分量信号IMF,将低频信号进行重构。然后根据各IMF的频率特性选取合适的预测模型。最后将各分量预测的结果相加就得到了最终预测值。对于高频随机分量IMF1,由于其波动性大,本文考虑温度及节假日因素,并采用神经网络和粒子群优化(PSO)方法对其进行组合预测,仿真结果表明该方案达到较好的预测效果。
论文最后以2006年四川省某地电力系统实际负荷数据为样本集进行建模和预测,并以相对误差为性能指标进行了检验。结果表明,该方法达到较高的预测精度。
Power system short-term load forecast is the basis of power system optimization running. It can affect safety, reliability and economy of power system operation. Thus, to find effective method has great importance for enhancing the prediction precision. Researchers have proposed many effective methods, but there is a lot of room for improvement. This paper use HHT to decompose the load datafirstly, then combine some popular methods,such as neural network、Support Vector Maehine、PSO to forecast the power system short-term load. The main work included:
Pretreatment on the load data, applying wright criteria to remove outliers and wavelet algorithm to denoise; use a methed to improve its inherent flaw modal-mixing. A series of IMFs with frequency from high to low can be obtained after decompose the load data, reconstruct the low-frequency signals, then according to each IMF's frequency feature we can chose appropriate model to forecast it. At last we can get the final forecast by add the predicted results of each IMF component. Due to IMF1 has large fluctuation, take temperature and weekday into consideration, otherwise neural network and PSO are used to optimize the combination weights. Simulation results indicate that this method has higher accuracy.
Finally, the actual data of power system load in a certain place in Sichuan Province in 2006 is used as the sample to set prediction model、test results and verify the accuracy of the model.
引文
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[4]叶瑰呁,刘勇.基于ARMA模型的电力系统负荷预测方法的研究[J].信息技术.2002,(6)：22-24
[5]康重庆,夏青,张伯明.电力系统负荷研究综述与发展方向的探讨[J].电力系统自动化.2004,12(28)：1-11
[6]孙洪波,徐国禹,秦翼鸿.模糊理论在电力系统符合预测中的应用[J].重庆大学学报.1994,17(1)：18-22
[7]Kadil MS, El-Debeliky SM, Hasanien NE. Long-term Load Forecasting for Fast Developing Utility Using a Knowledge-based Expert System. IEEE Trans on Power Systems.2002,17(2):491-496
[8]Bhattacharya TK, Basu TK. Medium Range Forecasting of Power System Load Using Modilied Kalman Filter and Walsh Transform. International Journal of Electrical Power and Energy System.1993,15(2):257-262
[9]Sfetsos. A Short-term Load Forecasting with a Hybrid Clustering Algorithm. IEEE Proceedings Communications.2003,150(3):257-262.
[10]王东生,曹磊.混沌、分形及其应用[M].中国科学技术出版社.1995
[11]徐军华,刘天琪.基于小波分解和人工神经网络的短期负荷预测[J].电网技术.2004,28(8)：30-33
[12]Dong Zhaoyang, Zhang Bailing,Huang Qian. A daptive neural network short-t erm load forecasting with wavelet decompositions[C]. In:IEEE Porto Power Tech Conference. Proto, Portugal:2001
[13]谢宏,陈志业,牛东晓.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[J].中国电机工程学报.2001,21(5)：5-10
[14]张大海.基于人工神经网络与小波理论的电力系统负荷预测研究[M].天津大学出版社.2002
[15]王永,徐建.神经元网络控制[M].机械工业出版社.1999
[16]金先级.人工神经网络导论讲义[M].华东理工大学出版社.1996
[17]周佃民,官晓宏,孙捷等.基于神经网络的电力系统短期负荷预测研究[J].电网 技术.2002,26(2)：10-13
[18]董景荣.基于模糊系统的非线性组合预测方法研究[J].理论与方法研究.1996,11(6)：25-28
[19]董景荣.基于小波网络的非线性组合预测方法研究[J].系统工程学报.2000,26(15)：383-388
[20]李元成,方庭建.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.2003,24(6)：154-159
[21]吴宏晓,侯志坚.基于免疫支持向量机方法的电力系统短期负荷预测[J].电网技术.2004,34(23)：47-50
[22]王洪波,朱启兵.基于EMD和LS-SVM的非平稳振动信号趋势预测[J].计算机工程与应.2008,44(16)：157-159
[23]刘慧婷.EMD方法的研究与应用[J].合肥：安徽大学硕士论文.2004
[24]Zhihui ZHU, Yunlian SUN, Huangqiang LI. Hybrid of EMD and SVMs for Short-Term Load Forecasting [J], IEEE International Conference on Control and Automation,2007,5.30-6.1:1044-1047
[25]王洪波,朱启兵.基于EMD和LS-SVM的非平稳振动信号趋势预测[J].计算机工程与应用.2008,44(16)：157-159
[26]谢景新,程春田,周桂红,孙玉梅.基于经验模式分解与混沌分析的直接多步预测模型[J].自动化学报,2008,34(6)：684-689
[27]李媛媛,牛东晓,乞建勋,刘达.基于因散经验模式分解的电力负荷混合预测方法[J].电网技术,2008,32(8)：58-62
[28]N.E. Huang, Z. Shen, S.R. Long, M.L. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung and H.H. Liu. The empirical mode decomposition and Hilbert spectrum for monlinear and non-stationary time series analysis[C]. Proc. Roy. Soc. London A, Vol.454, pp.903-995,1998
[29]刘霖雯,刘超,江成顺.EMD新算法及其应用[J].系统仿真学报2007,19(2)：10-15
[30]钟佑明.希尔伯特—黄变换局部瞬信号分析理论的研究[D].重庆：重庆大学博士论文,2002.
[31]LIHelong, YANGLihua, HUANGDaren.The study of the intermittency test filtering character of Hilbert-Huang transform. Mathematics and Computers in Simulation.2005,70:22-32
[32]宋立新,王祁,王玉静.具有间断事件检测和分离的经验模态分解方法[J].哈尔滨工程大学学报.2007,28(2)：178-182
[33]闻新,周露,王丹力.Matlab神经网络应用设计[M].科学出版社.2002
[34]董秀成,李芹,许强.BP算法在电力系统短期负荷预测中的应用[J].仪器仪表学报.2003,4(24)：54-58
[35]刘峤,李渝曾,张少华.RBF网络在节假日短期负荷预测中的应用[J].上海大学学报.2001,5(7)：32-36
[36]Vapnik.V.N. An oversee of statistical learning theory[J]. IEEE Transtion Neural NetworkS.1999,10(3):988-999
[37]高尚,杨靖宇著.群智能算法及其应用[M].北京：中国水利水电出版社.2006.
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