| 引用本文: | 彭显刚,赖家文,陈奕.基于聚类分析的客户用电模式智能识别方法[J].电力系统保护与控制,2014,42(19):68-73.[点击复制] |
| PENG Xian-gang,LAI Jia-wen,CHEN Yi.Application of clustering analysis in typical power consumption profile analysis[J].Power System Protection and Control,2014,42(19):68-73[点击复制] |
|
| 摘要: |
| 结合k-means、k-medoids、SOM以及FCM等聚类算法，构建了电力大客户典型用电模式的聚类分析模型，提出了一种评估聚类效果的新方法。首先通过分析电力客户用电指标数据及其特点，提出采用高斯滤波器对含“噪声”曲线数据进行平滑处理来获取客户用电数据。然后提出了聚类平均半径、平均直径和平均最小间距等3个评价指标，并以此为基础设计出一种评估聚类得分的新方法。最后使用聚类分析模型对某地区电力大客户日用电量曲线进行聚类分析，实现了地区典型用电模式的自动识别功能。实际算例分析结果表明，该评估方法物理概念清晰、简便、实用。 |
| 关键词: 用电模式分析 高斯核函数平滑 聚类效果评估 聚类分析 |
| DOI：10.7667/j.issn.1674-3415.2014.19.011 |
| 投稿时间：2013-12-31修订日期：2014-03-02 |
| 基金项目:广东省自然科学基金(10151009001000045)；南方电网科技项目(K-GD2012-214) |
|
| Application of clustering analysis in typical power consumption profile analysis |
| PENG Xian-gang,LAI Jia-wen,CHEN Yi |
| (School of Automation, Guangdong University of Technology, Guangzhou 510006, China;Zhanjiang Power Supply Bureau of Guangdong Power Grid Corporation, Zhanjiang 524005, China) |
| Abstract: |
| In order to gain the large power customers’ typical power consumption profiles in a power supply area, a new clustering evaluation method is presented and a clustering analysis framework based on k-means, k-medoids, self-organized maps (SOM) and Fuzzy C-Means (FCM) is built. It analyzes the characteristic of the electricity consumption data and uses the Gaussian smoothing method to reduce the noise in the data. Clusters average radius, clusters average diameter and clusters average minimum distance are proposed and used to design the clustering evaluation method. This framework is utilized to analyze the daily electricity consumption curves of the whole customers in a certain area, which can automatically recognize the number of clusters. The result shows this methodology is clear in physical conception, simple and practical. |
| Key words: power consumption profile analysis Gaussian smoothing clustering evaluation clustering analysis |
