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电网技术  2006 

基于数据挖掘多层次细节分解的负荷序列聚类分析

, PP. 51-56

Keywords: 数据挖掘,负荷时间序列,多层次细节分解聚类法,差分序列均方差,Kohonen神经网络,短期负荷预测

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Abstract:

提出了多层次细节分解的负荷聚类算法及其性能评估指标。该算法利用负荷序列间的差分序列均方差和欧氏距离形成交集优化判据;同时根据随机因素对负荷的敏感性加入对应参数要求来控制多层次细节分解聚类,对负荷曲线轮廓相似性细节程度聚类是提高预测精度的重要基础。笔者对所提出的聚类算法与一般欧氏距离聚类、Kohonen神经网络聚类算法进行了性能评估和比较,证明了该算法对季节性负荷具有高敏感性,对高温和气候因素与负荷之间的复杂相关性具有高识别能力,该聚类算法对提高负荷预测精度是有效的。

References

[1]  Sun Yaming,Wang Chenli,Zhang Zhisheng,et al.Clustering analysis of power system load series based on ant colony optimization algorithm[J].Proceedings of the CSEE,2005,25(18):40-45.
[2]  张智晟.基于多元理论融合的电力系统短期负荷预测的研究[D].天津:天津大学,2004.
[3]  姚李孝,宋玲芳,李庆宇,等.基于模糊聚类分析与BP网络的电力系统短期负荷预测[J].电网技术,2005,29(1):20-23.
[4]  Yao Lixiao,Song Lingfang,Li Qingyu,et al.Power system short-term load forecasting based on fuzzy clustering analysis and BP neural network[J].Power System Technology,2005,29(1):20-23.
[5]  陈耀武,汪乐宇,龙洪玉.基于组合式神经网络的短期电力负荷预测模型[J].中国电机工程学报,2001,21(4):79-82.
[6]  Chen Yaowu,Wang Leyu,Long Hongyu.Short-term load forecasting with modular neural networks[J].Proceedings of the CSEE,2001,21(4):79-82.
[7]  Tranchita C,Torres A.Soft computing techniques for short term load forecasting[C].2004 IEEE PES Power Systems Conference & Exposition,New York,USA,2004,1:497-502.
[8]  于之虹,郭志忠.数据挖掘与电力系统[J].电网技术,2001,25(8):58-62.
[9]  Yu Zhihong,Guo Zhizhong.Data mining and power system [J].Power System Technology,2001,25(8):58-62.
[10]  Patel P,Keogh E,Lin J,et al.Mining motifs in massive time series database[C].Proceedings of 2002 IEEE International Conference on Data Mining,Maebashi,Japan,2002:370-377.
[11]  Feng Li,Qiu Jiaju.Electrical load forecasting based on load patterns[J].Power System Technology,2005,29(4):23-26,40.
[12]  程其云,王有元,陈伟根.基于改进主成分分析的短期负荷预测方法[J].电网技术,2005,29(3):64-67.
[13]  Cheng Qiyun,Wang Youyuan,Chen Weigen.Modified principal component analysis based short-term load forecasting[J].Power System Technology,2005,29(3):64-67.
[14]  王志勇,郭创新,曹一家.基于模糊粗糙集和神经网络的短期负荷预测方法[J].中国电机工程学报,2005,25(19):7-11.
[15]  Wang Zhiyong,Guo Chuangxin,Cao Yijia.A method for short term load forecasting integrating fuzzy-rough set with artificial neural network[J].Proceedings of the CSEE,2005,25(19):7-11.
[16]  杨延西,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.
[17]  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.
[18]  孙雅明,王晨力,张智晟,等.基于蚁群优化算法的电力系统负荷序列的聚类分析[J].中国电机工程学报,2005,25(18):40-45.
[19]  Osowski S,Siwek K.The selforganizing neural network approach to load forecasting in the power system[C].International Joint Conference on Neural Networks,Washington DC,USA,1999,5:3401-3404.
[20]  Beccali M,Cellura M,Brano V L.Forecasting daily urban electric load profiles using artificial neural networks[J].Energy Conversion and Management,2004,45(18):2879-2900.
[21]  Fatima R,Jorge D,Vera F.A comparative analysis of clustering algorithms applied to load profiling[C].The Third International Conference on Machine Learning and Data Mining in Pattern Recognition,Leipzig,Germany,2003:73-85.
[22]  Duarte F J,Rodrigues F,Figueiredo V.Data mining techniques applied to electric energy consumers characterization[C].Proceedings of the Seventh International Conference on Artificial Intelligence and Soft Computing,Banff,Canada,2003:105-110.
[23]  Pujari A K,Rajesh K,Suresh R D.Clustering techniques in data mining —a survey[J].IETE Journal of Research,2001,47(1):19-28.
[24]  Gerbec D,Gasperic S,Smon I.Consumers’ load profile determination based on different classification methods[C].IEEE Power Engineering Society General Meeting,Toronto,Canada,2003,2:990-996.
[25]  刘小华,刘沛,张步涵,等.逐级均值聚类算法的RBFN模型在负荷预测中的应用[J].中国电机工程学报,2004,24(2):17-21.
[26]  Liu Xiaohua,Liu Pei,Zhang Buhan,et al.Application of RBFN model for load forecasting based on ranking means clustering [J].Proceedings of the CSEE,2004,24(2):17-21.
[27]  Pitt B D,Kitschen D S.Application of data mining techniques to load profiling[C].Proceedings of the 21st International Conference on Power Industry Computer Applications,Santa Clara,USA,1999:31-36.
[28]  李桂林,陈晓云.关于聚类分析中相似度的讨论[J].计算机工程与应用,2004,(31):64-66.
[29]  Li Guilin,Chen Xiaoyun.The discussion on the similarity of cluster analysis[J].Computer Engineering and Applications,2004,(31):64-66.
[30]  雷绍兰,孙才新,周湶,等.电力短期负荷的多嵌入维一阶局域预测[J].电网技术,2005,29(13):45-49.
[31]  Lei Shaolan,Sun Caixin,Zhou Quan,et al.Short-term load forecasting using one-rank local-region method in multi-dimension embedding phase space[J].Power System Technology,2005,29(13):45-49.
[32]  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.
[33]  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.
[34]  冯丽,邱家驹.基于电力负荷模式分类的短期电力负荷预测[J].电网技术,2005,29(4):23-26,40.

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