广东电力2017，Vol.30Issue(12)：91-96,6.DOI:10.3969/j.issn.1007-290X.2017.012.015
基于GMM聚类和SVM的用户负荷行业分类辨识方法
Identification Method for User Industry Classification Based on GMM Clustering and SVM
摘要
Abstract
User industry classification is the precondition for assessing demand side response capability,the paper presents a kind of identification method for user industry classification by making use of load data.The method adopts Gaussian mixture model (GMM) clustering algorithm to extract typical daily load curve for load data of every user,and uses support vector machine (SVM) algorithm to study relationship between user classification and its typical daily load curve based on training data and build the classification model for industry classification for new users.The method has realized high accuracy in identification for user industry classification and provided support for demand side management for the dispatching department.Analysis on load data about 290 users in one province in China verifies effectiveness of this method.关键词
需求侧响应/负荷分类/负荷曲线/支持向量机/GMM聚类Key words
demand side response/load classification/load curve/support vector machine/Gaussian mixture model (GMM)clustering分类
信息技术与安全科学引用本文复制引用
蔡秋娜,刘思捷,陆秋瑜..基于GMM聚类和SVM的用户负荷行业分类辨识方法[J].广东电力,2017,30(12):91-96,6.基金项目
广东电网有限责任公司科技项目(GDKJXM20162439) （GDKJXM20162439）
