摘要
用户行业分类是进行需求侧响应能力评估的前提,为此提出一种利用负荷数据进行用户行业判别的方法。对每一个用户的负荷数据采用高斯混合模型聚类算法提取其典型日负荷曲线,并利用支持向量机算法在训练集上学习用户类别与其典型日负荷曲线之间的关系,建立分类模型,并据此对新的用户进行行业分类。该方法实现了用户行业类型的高准确度辨识,为调度部门进行需求侧管理提供支撑。通过分析我国某省290个用户负荷数据,验证了该方法的有效性。
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 mix-ture model （GMM） clustering algorithm to extract typical daily load curve for load data of every user, and uses support vec-tor machine （SVM） algorithm to study relationship between user classification and its typical daily load curve based on train-ing data and build the classification model for industry classification for new u se r. The method has realized high accuracy in identification for user industry classification and provided support for demand side management for the dispatching depart-ment. Analysis on load data about 290 users in one province in China verifies effectiveness of this method.
出处
《广东电力》
2017年第12期91-96,共6页
Guangdong Electric Power
基金
广东电网有限责任公司科技项目(GDKJXM20162439)
关键词
需求侧响应
负荷分类
负荷曲线
支持向量机
GMM聚类
demand side response
load classification
load curve
support vector machine
Gaussian mixture model （GMM）clustering
