Load Forecasting Based on Improved K-means Clustering Algorithm

被引：0
|
作者：
Wang Yanbo [1 ,2 ]
Liu Li [1 ,2 ]
Pang Xinfu [1 ]
Fan Enpeng [3 ]
机构：
[1] Shenyang Inst Engn, Key Lab Energy Saving & Controlling Power Syst Li, Shenyang, Liaoning, Peoples R China
[2] Shenyang Inst Engn, Sch Elect Power, Shenyang, Liaoning, Peoples R China
[3] Chaoyang Power Supply Co, Chaoyang, Peoples R China
来源：
2018 CHINA INTERNATIONAL CONFERENCE ON ELECTRICITY DISTRIBUTION (CICED) | 2018年
基金：
中国国家自然科学基金;
关键词：
big data; load forecasting; k-means; clustering analysis;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
According to the data density of the data, select the initial cluster center to fully reflect the distribution characteristics of the data; In the iterative calculation of the new cluster center, the average distance between the data and the center of the class is used as the cluster center of the new iteration to eliminate the influence of the noise point. The cluster evaluation Index Hubert Index and D Index select the optimal cluster number to reflect the spatial distribution of data more accurately. By using the improved k-means algorithm to cluster the data of a variable table, the typical daily load characteristic data of four quarters vi ere obtained, and the short-term load prediction was carried out on this basis. The analysis shows that the load modeling method based on improved k-means is feasible and effective for grid simulation analysis.
引用
收藏
页码：2751 / 2755
页数：5
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