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| Load Forecasting Based on Support Vector Machine Analysis |
| Liu Qiming, Jiamalihan?Kumashi, Hua Dong, Li Pengfei |
| Electrical Engineering Institute of Xinjiang University, Urumqi 830047 |
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Abstract Support Vector Machine to solve the small sample, nonlinear, and had learning problems have a good advantage, combined with the Xinjiang region of fast load growth, changes in non-linear load conditions can be considered as a new load forecasting method applied to the practical work to improve the prediction accuracy, this article take the Xinjiang some practical power network data in support vector machine SVM prediction method to predict, by analysis of forecasting results prove the validity and feasibility of this method, embodies the practical application value.
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| Cite this article: |
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Liu Qiming,Jiamalihan?Kumashi,Hua Dong等. Load Forecasting Based on Support Vector Machine Analysis[J]. Electrical Engineering, 2013, 14(5): 37-39.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2013/V14/I5/37
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[1] 吕佳良.长春市电力市场负荷预测研究[D].北京:华北电力大学硕士学位论文, 2008.6:6-7.
[2] 赵希正.中国电力负荷特性分析与预测[M].1版.北京:中国电力出版社, 2001:51-55.
[3] 王登峰.地区电网分布式负荷预测研究与开发[D].天津大学硕士学位论文, 2007.6:4-5.
[4] 牛东晓, 等.电力负荷预测技术及其应用[M]. 2版.北京:中国电力出版社, 1998：1-15.
[5] 李鹏飞.几种电力负荷预测方法的比较[J].电气技术, 2011.
[6] 张学工.统计学习理论的本质[M].2版.北京:清华大学出版社, 2000.
[7] 张林, 刘先珊, 阴和俊.基于时间序列的支持向量机在负荷预测中的应用[J].电网技术, 2004：38-41.
[8] 王静娴.基于支持向量机的中短期电力负荷预测[D].华北电力大学硕士学位论文, 2008：5-7.
[9] 陈朴.模拟退火支持向量机算法研究及在电力负荷预测中的应用[D]. 哈尔滨：哈尔滨工业大学硕士学位论文, 2006:26-27.
[10] 李国正, 王猛.支持向量机导论[M].北京:电子工业出版社, 2006:82-107.
[11] 王晓红, 吴德会.基于 WLS-SVM 回归模型的电力负荷预测[J].微计算机信息, 2008(2).
[12] 张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000(1).
[13] 潘锋, 程浩忠.基于 RBF 核函数的 SVM 方法在短期电力负荷预测中的应用[J].供用电, 2006(1).
[14] 蒋喆.支持向量机在电力负荷预测中的应用研究[J].计算机仿真, 2010(8). |
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2013, Vol. 14 
