| 基于连续时间段聚类的支持向量机风电功率预测方法 |
| |
| 引用本文： | 丁志勇,杨苹,杨曦,张臻.基于连续时间段聚类的支持向量机风电功率预测方法[J].电力系统自动化,2012,36(14):131-135. |
| |
| 作者姓名： | 丁志勇 杨苹 杨曦 张臻 |
| |
| 作者单位： | 广东省绿色能源技术重点实验室,华南理工大学电力学院,广东省广州市511458 |
| |
| 基金项目： | 发明专利 专利号201110243715.6 |
| |
| 摘 要： | 提出了一种基于连续时间段聚类的支持向量机风电功率预测方法。通过2次聚类把全年分为若干个类型的连续时间段,并对同类型时间段使用支持向量机建模,建立后的模型用于其他年份对应时间段的预测。与神经网络相比,支持向量机建模方法避免了局部最优。利用国内某风电场数据进行对比实验,证明了所述方法的有效性。
|
| 关 键 词： | 风力发电 功率预测 时间段聚类 支持向量机 |
| 收稿时间： | 8/6/2011 10:37:11 PM |
| 修稿时间： | 6/15/2012 9:37:34 PM |
Wind Power Prediction Method Based on Sequential Time Clustering Support Vector Machine |
| |
| DING Zhiyong,YANG Ping,YANG Xi,ZHANG Zhen.Wind Power Prediction Method Based on Sequential Time Clustering Support Vector Machine[J].Automation of Electric Power Systems,2012,36(14):131-135. |
| |
| Authors: | DING Zhiyong YANG Ping YANG Xi ZHANG Zhen |
| |
| Affiliation: | (Key Laboratory of Clean Energy Technology of Guangdong Province,School of Electric Power, South China University of Technology,Guangzhou 511458,China) |
| |
| Abstract: | A wind power prediction method based on sequential time clustering support vector machine(SVM) is proposed.Each year is divided into several continuous time series by clustering twice,with one catching the daily similarity to build model by SVM and the proposed model is used to predict statistics of time series corresponding to other years.The SVM model can avoid converging into a local optimal zone compared to neural networks method.Experiments on a wind farm show the effectiveness of the proposed method. |
| |
| Keywords: | wind power generation power prediction sequential time clustering support vector machine (SVM) |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《电力系统自动化》浏览原始摘要信息 |
|
点击此处可从《电力系统自动化》下载全文 |
|
