| 引用本文: | 傅军栋,杨 姚,罗善江.智能小区居民用电负荷特征权重分析[J].电力系统保护与控制,2016,44(18):41-45.[点击复制] |
| FU Jundong,YANG Yao,LUO Shanjiang.Residential electricity load features weighting analysis in smart community[J].Power System Protection and Control,2016,44(18):41-45[点击复制] |
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| 摘要: |
| 以往对智能小区居民用电行为聚类分析时，存在着负荷特征选择与权重计算描述不足的问题。为了提高居民用电行为聚类分析的准确率，降低聚类分析运行时间，提出一种基于ReliefF算法建立的以峰时耗电率、日负荷峰值时刻、谷时耗电率、日负荷周期数、日最小负荷率等特征的数据模型。该模型可以对海量居民用电行为数据进行处理，并通过k-means算法对其进行聚类分析。实验数据来源为已建成的智能小区，结果准确率达94.61%，证明了基于ReliefF算法建立的特征数据模型在居民用电行为类分析中是有效的。 |
| 关键词: 用电行为 聚类分析 负荷特征 数据模型 |
| DOI：10.7667/PSPC151714 |
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| 基金项目: |
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| Residential electricity load features weighting analysis in smart community |
| FU Jundong,YANG Yao,LUO Shanjiang |
| (School of Electrical Engineering, East China Jiaotong Uinversity, Nanchang 330013, China) |
| Abstract: |
| In order to solve the described insufficient problem of load feature selection and weight calculation in the past clustering analysis of residential electricity behavior, enhance the accuracy of clustering analysis in residential electricity behavior and reduce the time of clustering analysis operation, a data model based on ReliefF algorithm is proposed. The data model is characterized by electricity consumption rate during peak hour, the peak load time, the valley of the power, daily load cycles, the minimum load rate feature, and so on. The massive data of residential electricity behavior can be processed by the model, and clustering analysis of the model is made through k-means algorithm. Experimental data is obtained from a built-up smart community, and the result accuracy reaches to 94.61%, showing the proposed model based on ReliefF algorithm in clustering analysis of residential electricity behavior is effective. |
| Key words: electricity consumption behavior clustering analysis load characteristic data model |
