| 基于模糊聚类与RBF网络的短期负荷预测 |
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| 引用本文： | 王平,张亮,陈星莺.基于模糊聚类与RBF网络的短期负荷预测[J].电力系统保护与控制,2006,34(10):64-67. |
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| 作者姓名： | 王平 张亮 陈星莺 |
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| 作者单位： | 河海大学电气工程学院 江苏南京210098 |
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| 摘 要： | 采用模糊聚类分析方法,应用隶属度来描述负荷与影响负荷变化因素之间的关系,得到一批与预测日在样本信息上类似的历史日;改进RBF网络的训练算法,增强RBF网络的局部逼近能力和泛化能力,采用由模糊聚类分析获得的样本对RBF网络进行训练,在不需大量训练样本的前提下实现对短期负荷的预测。对浙江省某地区电网的实际负荷数据仿真结果表明:该方法预测的日平均相对误差为1.91%,预测准确度为97.41%。
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| 关 键 词： | 模糊聚类 隶属度 RBF网络 短期负荷预测 |
| 文章编号： | 1003-4897(2006)10-0064-04 |
| 收稿时间： | 2005-10-19 |
| 修稿时间： | 2006-12-01 |
Short-term load forecasting based on fuzzy cluster and RBF network |
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| WANG Ping, ZHANG Liang, CHEN Xing-ying.Short-term load forecasting based on fuzzy cluster and RBF network[J].Power System Protection and Control,2006,34(10):64-67. |
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| Authors: | WANG Ping ZHANG Liang CHEN Xing-ying |
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| Affiliation: | Hohai University, Nanjing 210098, China |
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| Abstract: | In this paper,a short-term load forecasting method based on fuzzy cluster and RBF network is presented.By using fuzzy cluster theory,membership degree is applied to describe the correlative relation among the loads and their influencing factors,and gain a set of historical days which have similar information of the day to be forecasted.By training RBF network using the information of selected days,short-term load forecasting without large number of samples can be realized.The results of calculation example show that the daily mean error is 1.91% and daily precision is 97.41%. |
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| Keywords: | fuzzy cluster membership degree radial basis function network short-term load forecasting |
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