| 短期负荷预测的聚类组合和支持向量机方法 |
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| 引用本文： | 梁建武,陈祖权,谭海龙.短期负荷预测的聚类组合和支持向量机方法[J].电力系统及其自动化学报,2011,23(1):34-38. |
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| 作者姓名： | 梁建武 陈祖权 谭海龙 |
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| 作者单位： | 中南大学信息科学与工程学院,长沙,410083 |
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| 摘 要： | 为提高电力系统短期负荷预测的精度,提出了一种基于聚类组合和支持向量机的短期负荷预测方法.该方法用SOM网络训练规格化的特征数据并获得初始聚类中心,将初始聚类中心作为C-均值算法的输入,并用DB指数评价聚类结果以获得最佳聚类数,通过训练可得相似日样本,最后选择合适的参数和核函数构造支持向量机模型来进行逐点负荷预测.预测结...
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| 关 键 词： | 短期负荷预测 聚类组合 SOM网络 C-均值 相似日 |
Application of Clustering Combination and Support Vector Machine in Short-term Load Forecasting |
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| LIANG Jian-wu,CHEN Zu-quan,TAN Hai-long.Application of Clustering Combination and Support Vector Machine in Short-term Load Forecasting[J].Proceedings of the CSU-EPSA,2011,23(1):34-38. |
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| Authors: | LIANG Jian-wu CHEN Zu-quan TAN Hai-long |
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| Affiliation: | LIANG Jian-wu,CHEN Zu-quan,TAN Hai-long(Institute of Information Science and Engineering,Central South University,Changsha 410083,China) |
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| Abstract: | In order to improve the accuracy of power system short-term load forecasting,a method of short-term load forecasting based on clustering combination and support vector machine is proposed.First,the standardized data are trained through SOM network and the initial clustering center are acquired,then the initial clustering centre is used as the input values of the C-means algorithm,and the best number of the clustering is obtained through DB index,the samples of similar days are acquired through training.Fina... |
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| Keywords: | short-term load forecasting clustering combination SOM network C-means similar day |
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