李柏新,雷才嘉,方兵华,黄裕春,贾巍,马乙歌.基于日负荷指标及改进分布式K-means聚类的用户用电规律研究[J].电测与仪表,2023,60(10):101-111. Li Baixin,Lei Caijia,Fang Binghua,Huang Yuchun,Jia Wei,MaYige.Research on typical electricity consumption law based on daily load indicator and improved distributed K-means clustering[J].Electrical Measurement & Instrumentation,2023,60(10):101-111.
基于日负荷指标及改进分布式K-means聚类的用户用电规律研究
Research on typical electricity consumption law based on daily load indicator and improved distributed K-means clustering
Load clustering can not only provide high-quality data for fine load forecasting, but also help carry out user behavior analysis according to the law of electricity consumption. In order to meet the challenge of processing massive data, a dimension reduction and improved K-means clustering algorithm based on daily load indicators is proposed in this paper. Firstly, the original high-dimensional load data is converted into low-dimensional data by establishing a daily load indicator. Then, the distributed K-means algorithm improved by the entropy weight method is used to cluster the low-dimensional data in order to discover hidden typical load types. Finally, combing with the example, the electricity consumption law is analyzed according to the obtained typical load, and it is matched with the actual user type, and the four typical electricity consumption laws are summarized.
