基于PK-Means算法的用电模式聚类

Clustering of Power Consumption Patterns Based on PK-Means Algorithm

摘要: 用电模式聚类是电网需求侧管理，负荷预测、电力系统规划等工作的重要基础，对电力系统的分析、运行、规划都具有重要意义。针对传统的K-Means算法在进行用电模式聚类时没有有效利用时序特征的问题，提出了一种基于K-Means算法改良的时间序列聚类算法PK-Means,并在SSE评价指标基础上进行了改进，提出了一种用于时间序列聚类算法的评价指标累计相似度(CS)，通过皮尔逊相关系数的引入，PK-Means算法在用电模式聚类的场景下相较于传统的K-Means取得了更好的聚类效果。

Abstract: Clustering of power consumption patterns is an important basis for power grid demand side management, load forecasting, and power system planning, and is of great significance to the analysis, operation, and planning of power systems. Aiming at the problem that the traditional K-Means algorithm does not effectively use time series features when clustering electricity consumption patterns, an improved time series clustering algorithm PK-Means based on the K-Means algorithm is proposed, and based on the SSE evaluation index an improvement was made, and an evaluation index cumulative similarity (CS) for time series clustering algorithm was proposed. Through the introduction of Pearson correlation coefficient, PK-Means in the scenario of electricity consumption pattern clustering compared with the traditional K-Means achieves better clustering results.