基于改进K-Means++分时电量聚类与行业用电行为分析

由于能源互联网中分时电量分布不仅为分时电价的制定提供依据还代表着用户的用电意识,提出改进K-Means++的聚类算法与典型用户筛选模型对分时电量进行挖掘。首先对K-Means++进行改进,利用模拟退火算法(simulated annealing, SA)与中位数阈值分割自动确定聚类初始质心与聚类数,弗雷歇与欧式距离的加权复合作为相似性的度量,权值由信息熵与层次分析法(analytic hierarchy process, AHP)确定。然后对分时电量进行聚类,从每簇聚类结果中依据典型用户筛选模型筛选典型用户,得到3种用电类型,最后从主要用电类型与用电类型转变的角度对行业用电行为分析,得到不同行业相同的用电行为。有助于供电侧初步掌控区域性行业用电群体的用电特征,为精细有序的用电管理做准备。

Based on improved K-Means++ time-sharing power clustering and industry power consumption behavior analysis

Since the distribution of time-of-use electricity in the energy Internet not only provides a basis for the formulation of time-of-use electricity prices, but also represents users" awareness of electricity consumption, an improved K-Means++ clustering algorithm and a typical user screening model are proposed to mine time-of-use electricity. Firstly, K-Means++ is improved, using simulated annealing algorithm (SA) and median threshold segmentation to automatically determine the initial centroid and number of clusters. The weighted compound of Frecher and Euclidean distance is used as a measure of similarity, and the weight is determined by Information entropy and Analytic Hierarchy Process (AHP) determine. Then the time-sharing power is clustered, and typical users are screened according to the typical user screening model from the clustering results of each cluster, and 3 types of power consumption are obtained. Finally, the industry power consumption behavior is analyzed from the perspective of main power consumption and the transformation of the power consumption. Analysis and get the same electricity consumption behavior in different industries. It is helpful for the power supply side to initially control the electricity consumption characteristics of the regional industry electricity consumption groups, and prepare for fine and orderly electricity management.