基于VAE-Kmeans算法的台区聚类精准画像技术

Precision Profiling Technology of Transformer Areas Clustering Based on VAE-Kmeans Algorithm

构建起台区用电的精准画像,刻画台区负荷特性及用电模式,对精细化负荷预测、负荷波动分析溯源及台区业务场景能够起到精准指导作用。为此,提出一种基于VAE(变分自编码)-Kmeans算法的台区聚类精准画像技术。采用变分自编码、Kmeans聚类和时间序列相似度算法,从多种维度提取台区和行业负荷的典型曲线,通过综合评估确定最相似的行业负荷,为台区分配相应的画像标签。以占比较大的居民台区为例,构建基于聚类分析和决策树算法的深度挖掘和精准描绘策略,形成了14种城乡居民用电台区精准画像类别,为精细化负荷预测、差异化台区运维管理策略提供了坚实基础。

Building a precise portrait of electricity consumption in the substation area,depicting the load characteristics and electricity consumption patterns of the substation area,can provide precise guidance for refined load forecasting,load fluctuation analysis and tracing,and substation business scenarios.Therefore,this paper proposes a precision profiling technology of transformer areas clustering based on VAE(variational auto-encoder)-Kmeans algorithm.Firstly,VAE,Kmeans clustering and time series similarity algorithms are used to extract the typical curves of load for station areas and industries from multiple dimensions.Secondly,the most similar industry load is determined through comprehensive evaluation,and the corresponding portrait label is assigned to the transformer area.Finally,taking the relatively large residential transformer area as an example,a deep mining and accurate description strategy based on cluster analysis and decision tree algorithm are constructed,and 14 sets of accurate portrait categories of urban and rural residential transformer areas are formed,which provides a solid foundation for fine load forecasting and differentiated transformer operation and maintenance management strategies.