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-  2018 

考虑影响因素的短期负荷预测核函数ELM方法 Kernel function ELM method for short-term load forecasting considering influencing factors

Keywords: 结构风险最小化原则,极限学习机,负荷影响因素,核函数极限学习机,最小二乘支持向量机模型

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Abstract:

结合结构风险最小化原则,使用核函数映射代替基本极限学习机(ELM)模型中的隐层节点特征映射,在考虑温度、相对湿度、日期类型、历史负荷等影响因素情况下提出了基于核函数极限学习机模型的短期负荷预测新方法.该方法具有较强的泛化能力,并能避免基本ELM模型可能产生的过学习现象.对实际负荷数据进行预测分析,其研究结果表明核函数极限学习机模型的预测精度要优于基本ELM模型、最小二乘支持向量机模型以及BP神经网络模型.同时也验证了核函数极限学习机方法用于短期负荷预测中的可行性和有效性

References

[1]  康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.Kang Chongqing,Xia Qing,Liu Mei.Power System Load Forecasting[M].Beijing:China Power Press,2007.
[2]  钟光科.偏最小二乘回归分析在短期负荷预测中的应用[D].邯郸:河北工程大学,2011.Zhong Guangke.Applications of partial least squares regression analysis in short-term load forecasting[D].Handan:Hebei University of Engineering,2011.
[3]  张思远,何光宇,梅生伟,等.基于相似时间序列检索的超短期负荷预测[J].电网技术,2008,32(12):56-59.Zhang Siyuan,He Guangyu,Mei Shengwei,et al.Short-term load forecasting based on similarity search in time-series[J].Power System Technology,2008,32(12):56-59.
[4]  耿艳.基于最小二乘支持向量机的短期负荷预测方法及应用研究[D].济南:山东大学,2008.Geng Yan.Short-term load forecasting methods and applied research based on least squares support vector machine[D].Jinan:Shandong University,2008.
[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.Li Yuancheng,Fang Tingjian,Yu Erkeng.Study of support vector machines for short-term load forecasting[J].Proceedings for the CSEE,2003,23(6):55-59.
[6]  Huang G B,Zhou H M,Ding X J,et al.Extreme learning machine for regression and multi-class classification[J].IEEE Transactions on Systems,Man,and Cybernetics,2012,42(2):513-529.
[7]  邓万宇,郑庆华,陈琳.神经网络极速学习方法[J].计算机学报,2010,33(2):279-287.Deng Wanyu,Zheng Qinghua,Chen Lin,et al.Research on extreme learning of neural networks[J].Chinese Journal of Computers,2010,33(2):279-287.
[8]  程松,闫建伟,赵登福.短期负荷预测的集成改进极端学习机方法[J].西安交通大学学报,2009,43(2):106-110.Cheng Song,Yan Jianwei,Zhao Dengfu,et al.Shortterm load forecasting method based on ensemble improved extreme learning machine[J].Journal of Xi’an Jiaotong University,2009,43(2):106-110.
[9]  刘学艺,李平,郜传厚.极限学习机的快速留一交叉验证算法[J].上海交通大学学报,2011,45(8):1140-1145.Liu Xueyi,Li Ping,Gao Chuanhou.Fast leave-oneout cross-validtion algorithm for extreme learning machine[J].Journal of Shanghai Jiaotong University,2011,45(8):1140-1145.
[10]  Cawley G C,Talbot N L.Fast exact leave-one-out cross-validation of sparse least-squares support vetor machines[J].Neural Networks,2004,17(10):1467-1475.
[11]  王亮红.基于时间序列分解技术的电力负荷预测乘积模型[J].东北电力大学学报,2013,33(6):45-47.Wang Lianghong.Product model for load forecasting based on decomposition technique in time series[J].Journal of Northeast Electric Power University,2013,33(6):45-47.
[12]  向峥嵘,王学平.基于小波-神经网络的电力系统短期负荷预测[J].系统仿真学报,2008,20(18):5018-5020.Xiang Zhengrong,Wang Xueping.Forecasting approach to short-term load using wavelet decomposition and artificial neural network[J].Journal of System Simulation,2008,20(18):5018-5020.
[13]  王捷,吴国忠,李艳昌.蚁群灰色神经网络组合模型在电力负荷预测中的应用[J].电力系统保护与控制,2009,37(2):48-52.Wang Jie,Wu Guozhong,Li Yanchang.Application of ant colony gray neural network combined forecasting model in load forecasting[J].Power System Protection and Control,2009,37(2):48-52.
[14]  Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1-3):489-501.
[15]  Vapnik.统计学习理论的本质[M].张学工,译.北京:清华大学出版社,2000:63-67.Vapnik.The Nature of Statistical Learning Theory[M].Zhang Xuegong,Translated.Beijing:Tsinghua University Press,2000:63-67.

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