电力系统自动化2019，Vol.43Issue(1)：110-118,9.DOI:10.7500/AEPS20180128006
采用自适应分段聚合近似的典型负荷曲线形态聚类算法
Shape Clustering Algorithm of Typical Load Curves Based on Adaptive Piecewise Aggregate Approximation
摘要
Abstract
The dimension reduction and classification of massive load data are the premise of critical load information extraction and deep data mining.According to the shape characteristics of load curves, this paper proposes a shape clustering algorithm of load curves based on the adaptive piecewise aggregate approximation method with variable temporal resolution.The information of the ramp events and slope-extracted edge point is collected to quantify the shape characteristics and fluctuation level of load curves.Then the users'daily load data sets with variable temporal resolution are reconstructed by the adaptive piecewise aggregate approximation algorithm.Further, the k-shape algorithm is applied in the clustering of daily load curves according to the shape characteristics of load curves.In the k-shape algorithm, the distance measurement method based on the similarity of curve shape is regard as the similarity criterion, and the cluster center is calculated based on the Steiner's sequence optimization method.The practicality and effectiveness of the proposed algorithm in data dimension reduction and load clustering are verified by simulation data and measured data.关键词
电力负荷/曲线聚类/k-shape算法/自适应分段聚合近似Key words
power load/curve clustering/k-shape algorithm/adaptive piecewise aggregate approximation (APAA)引用本文复制引用
王潇笛,刘俊勇,刘友波,许立雄,马铁丰,胥威汀..采用自适应分段聚合近似的典型负荷曲线形态聚类算法[J].电力系统自动化,2019,43(1):110-118,9.基金项目
国家自然科学基金资助项目(51437003) （51437003）
国家重点研发计划资助项目(2017YFE0112600) This work is supported by National Natural Science Foundation of China (No. 51437003) and National Key R&D Program of China (No. 2017YFE0112600). （2017YFE0112600）
