基于知识图谱与改进FCM算法的电力用户数据聚类分析方法

Power User Data Clustering Analysis Method Based on Knowledge Graph and Improved FCM Algorithm

摘要: 针对电力用户数据聚类分析中存在的特征关联性弱、业务解释性差等问题,提出了一种融合知识图谱与改进FCM算法的方法。首先构建电力领域知识图谱,实现用户用电特征的语义关联建模;其次设计多目标优化策略改进FCM算法,增强聚类性能;最终建立完整的电力用户画像模型。实验结果表明,该方法在聚类精度、算法鲁棒性、业务一致性等方面均显著优于传统方法,为电力企业开展精准营销、需求响应等业务提供了可靠的技术支撑。

Abstract: This study develops an innovative clustering method combining knowledge graph and optimized FCM algorithm for power user analysis. The approach constructs a power-specific knowledge graph to model feature relationships and enhances FCM through multi-objective optimization. Experimental results show superior clustering accuracy and business applicability compared to conventional methods, offering valuable support for power marketing and demand-side management.