电网技术2018，Vol.42Issue(5)：1605-1612,8.DOI:10.13335/j.1000-3673.pst.2017.1215
基于非参数核密度估计和改进谱多流形聚类的负荷曲线分类研究
Study on Load Curve's Classification Based on Nonparametric Kernel Density Estimation and Improved Spectral Multi-Manifold Clustering
摘要
Abstract
Aiming at the question of clustering load curves consisting of high-dimensional data, a clustering method based on improved spectral multi-manifold clustering (SMMC) was presented, including extraction of typical daily load curve, clustering of load curve and evaluation of clustering effect. Firstly, user's load characteristic index was extracted, and the typical daily load curve was extracted with nonparametric kernel density estimation method. In order to obtain similarity matrix of improved SMMC algorithm, canonical warping distance was used to measure curves' similarity and Gaussian kernel was used to calculate local similarity. After clustering, clustering validity indexes were adopted to evaluate clustering results and algorithm performance from three aspects: clustering effect, algorithm stability and computing time. A number of users' load data in a certain area were taken as an example for clustering analysis. Thereby rationality and superiority of the typical daily load curve extraction method and the improved SMMC algorithm were verified.关键词
非参数核密度估计/典型日负荷曲线/改进谱多流形聚类/时间翘曲距离/相似性矩阵Key words
nonparametric kernel density estimation/typical daily load curve/improved spectral multi-manifold clustering/canonical warping distance/similarity matrix分类
信息技术与安全科学引用本文复制引用
高亚静,孙永健,杨文海,薛伏申,孙彦萍,梁海峰,李鹏..基于非参数核密度估计和改进谱多流形聚类的负荷曲线分类研究[J].电网技术,2018,42(5):1605-1612,8.基金项目
国家自然科学基金项目(51607068) （51607068）
教育部中央高校基本科研业务费专项资金资助项目(2018MS082).Project Supported by National Natural Science Foundation of China (51607068) （2018MS082）
Fundamental Research Funds for the Central Universities (2018MS082). （2018MS082）
