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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 电力系统 输电工程 升压算法 电力能源 electricity price forecasting support vector regression boosting algorithm ensemble model gen-eralization capability
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

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

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