基于信息熵分段聚合近似和谱聚类的负荷分类方法

发布时间：2018-01-17 19:19

本文关键词：基于信息熵分段聚合近似和谱聚类的负荷分类方法　出处：《中国电机工程学报》2017年08期 　论文类型：期刊论文

【摘要】：居民和商业负荷参与需求响应项目时,负荷数据日趋多维化和海量化,需要对其进行降维分类处理。提出一种基于信息熵分段聚合近似(information entropy piecewise aggregate approximation,IEPAA)和谱聚类的负荷分类方法。首先采用IEPAA对典型日负荷数据集进行可变时间分辨率重表达,进一步采用基于距离和曲线形态的双尺度相似性度量谱聚类算法进行聚类处理,从而获得合理的负荷分类结果。利用商业办公楼宇中央空调机组的典型日负荷数据对所提方法进行了验证,表明该方法在数据降维、负荷分类有效性、稳定性和降低运算量等方面均具有优势。
[Abstract]:When residents and commercial loads participate in demand response projects, load data are increasingly multidimensional and quantitative. It is necessary to reduce the dimensionality of the classification. This paper presents a piecewise aggregation approximation based on information entropy. Information entropy piecewise aggregate approximation. IEPAA) and spectral clustering methods for load classification. Firstly, the typical daily load data sets are reexpressed with IEPAA with variable time resolution. Furthermore, a two-scale similarity measurement spectral clustering algorithm based on distance and curve morphology is used to process the clustering. Using the typical daily load data of the central air-conditioning unit in commercial office buildings, the proposed method is verified, which shows that the method is effective in reducing the data dimension and classifying the load. Stability and reduced computational complexity have advantages.
【作者单位】：上海电力学院电气工程学院;上海电器科学研究所;安徽大学电气工程与自动化学院;
【基金】：国家自然科学基金项目(51207088) 上海市科委科创项目(14DZ1201602) 上海绿色能源并网工程技术研究中心(13DZ2251900) 上海市教委曙光计划(15SG50) 国网公司科技项目(SGRI-DL-71-14-004)~~
【分类号】：TM714
【正文快照】： Shanghai Green Energy Grid Connected Technology Engineering ResearchCenter(13DZ2251900);Shanghai Municipal Education Commission(15SG50);The State Grid Technology Project(SGRI-DL-71-14-004).0引言随着我国电力体制改革深化推进,特别是六大新电改配套文件的下发,

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