基于改进蚁群算法的电力负荷半监督聚类

计量通信技术的发展使收集的用户负荷信息越来越准确,从而提供了负荷用电特性聚类分析的数据基础。为了解决电力负荷聚类应用场景中需要聚类结果与典型负荷类别尽可能相似的问题,以蚁群聚类算法为基础,采用典型负荷曲线作为先验信息,将评估聚类效果的指标和聚类中心与典型负荷曲线的距离2个因素构成优度指标来代替传统的均方误差,以此来更新信息素矩阵,设计了一种基于改进蚁群聚类的半监督聚类算法。通过某省工业用户2017年的日负荷数据分析验证了聚类结果不仅向原有的标识样本类型靠近,而且兼顾同类型样本差异小、不同类型样本差异大,具有良好的聚类效果。

Research on Power Load Semi-supervised Clustering Based on Improved Ant Colony Algorithm

The development of metrology communication technology enables the user’s load information to be accurately collected, and the clustering analysis of the power consumption characteristics of the load can be performed. In order to solve the problem that load clustering application scenarios need clustering results as similar as possible to initial cluster center, two improved ant colony clustering algorithms are designed on the basis of the ant colony clustering algorithm. The two factors that determine the clustering effect and the distance between the cluster center and the initial cluster center constitute the fitness index instead of the traditional mean square error for updating pheromone matrix. The example analysis shows that the algorithm can solve this kind of application scenario well and has good clustering effect.