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SVR-Boosting ensemble model for electricity price forecasting in electric power market

SVR-Boosting ensemble model for electricity price forecasting in electric power market
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摘要 A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boosting) is presented in this paper for electricity price forecasting in electric power market. In the light of characteristics of electricity price sequence, a new triangular-shaped 为oss function is constructed in the training of the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accuracy, and possess comparatively satisfactory generalization capability. A revised support vector regression （SVR） ensemble model based on boosting algorithm （SVR-Boosting） is presented in this paper for electricity price forecasting in electric power market. In the light of characteristics of electricity price sequence, a new triangular-shaped ）为 oss function is constructed in the training of the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accuracy, and possess comparatively satisfactory generalization capability.
作者 周佃民 高琳 管晓宏 高峰
机构地区 Energy Department Systems Engineering Institute
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第1期90-94,共5页 哈尔滨工业大学学报（英文版）
基金 Sponsored by the National Outstanding Young Investigator Grant (Grant No6970025) the Key Project of National Natural Science Foundation (GrantNo59937150) 863 High Tech Development Plan (Grant No2001AA413910) of China and the Key Project of National Natural Science Foundation(Grant No59937150) the Project of National Natural Science Foundation (Grant No60274054)
关键词 electricity price forecasting support vector regression boosting algorithm ensemble model gen-eralization capability 电力系统 输电工程 升压算法 电力能源
分类号 TM73 [电气工程—电力系统及自动化]
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参考文献13

  • 1高琳,高峰,管晓宏,周佃民.电力系统短期负荷预测的多神经网络Boosting集成模型[J].西安交通大学学报,2004,38(10):1026-1030. 被引量：8
  • 2李建民,张钹,林福宗.支持向量机的训练算法[J].清华大学学报（自然科学版）,2003,43(1):120-124. 被引量：46
  • 3Eric Bauer,Ron Kohavi.An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants[J].Machine Learning (-).1999(1-2)
  • 4Zhang Pingan,Guan Xiaohong.Fuzzy modeling for electri- cal market price forecasting[].Intelligent Control and Auto- mation Proceedings of the rd World Congress.2000
  • 5Gao Feng,Guan Xiaohong,Cao Xi-ren,et al.Forecasting power market clearing price and quantity using a neuralnetwork method[].IEEE Power Engineering Review.2000
  • 6Shevade S K,Keerthi S S,Bhattacharyya C,et al.Im- provements to the SMO algorithms for SVM regression[].IEEE Transactions on Neural Networks.2000
  • 7Smola A,Sch lkopf B.A Tutorial on Support Vector Re- gression[].Technical Report NC -TR - - Neu- roCOLT.1998
  • 8Freund Y,Schapire R E.A decision-theoretic generaliza- tion of on-line learning and an application to Boosting[].Journal of Computer and System Sciences.1997
  • 9Bauer E,Kohavi R.An empirical comparison of voting classification algorithms: Bagging, Boosting[].Machine Learning.1999
  • 10Ridgeway G,Mafigan D,Richardson T.Boosting method- ology for regression problems[].th Int Workshop on Artifi- cal Intelligence and Statistics.1999

二级参考文献33

  • 1VapnikV.统计学习理论的本质[M].北京：清华大学出版社,2000..
  • 2Khotanzad A. ASTLF-a neural network based electric load forecasting system [J]. IEEE Trans on Neural Network, 1997, 8(4): 835-846.
  • 3Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to Boosting [J]. Journal of Computer and System Sciences, 1997, 55(1):119-139.
  • 4Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: Bagging, Boosting [J]. Machine Learning, 1999, 36(1-2): 105-139.
  • 5Ridgeway G, Madigan D, Richardson T. Boosting methodology for regression problems [A]. 7th Int Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, USA, 1999.
  • 6Drucker H. Improving regressors using Boosting techniques [A]. The Fourteenth International Conference on Machine Learning, Morgan Kaufmann, USA,1997.
  • 7Osuna E,Freund R,Girosi F. Support Vector Machines: Training and Application [R]. CBCL Paper #144 / AI Memo #1602,Cambridge,MA: Massachusetts Institute of Technology,AI Lab,1997.
  • 8Osuna E,Freund R,Girosi F. An improved training algorithm for support vector machines [A]. Principe J,Gile L,Morgan N,et al. Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing [C]. IEEE,1997. 276-285.
  • 9Joachims T. Making large-scale support vector machine learning practical [A]. Scholkopf B,Burges C,Smola A. Advances in Kernel Methods - Support Vector Learning [C]. Cambridge,MA: MIT Press,1999. 169-184.
  • 10LIN Chihjen. On the convergence of the decomposition method for support vector machines [J]. IEEE Transactions on Neural Networks,2001,12(6): 1288-1298.

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Journal of Harbin Institute of Technology(New Series)
2008年 第1期

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