论文标题
聚集住宅电力消耗数据，以创建原型捕获南非家庭行为的原型
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
论文作者
论文摘要
聚类经常在能源领域中用于识别家庭的主要电力消耗模式，这些模式可用于为长期能源计划构建客户原型。但是，选择一组有用的集群需要广泛的实验和域知识。虽然内部聚类验证措施在电力域中得到了很好的确定，但它们限于选择有用的簇。基于南非的申请案例研究，我们提出了一种将隐性专家知识形式化为外部评估措施的方法，以创建客户原型，以捕捉住宅用电消耗行为的可变性。通过以结构化的方式组合内部和外部验证措施，我们能够根据它们为应用程序提供的效用来评估聚类结构。我们在用例中验证所选簇，在这种情况下，我们成功地重建了以前由专家开发的客户原型。我们的方法显示了数据科学家的透明和可重复的群集排名和选择的希望，即使他们的领域知识有限。
Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters. Based on an application case study in South Africa, we present an approach for formalising implicit expert knowledge as external evaluation measures to create customer archetypes that capture variability in residential electricity consumption behaviour. By combining internal and external validation measures in a structured manner, we were able to evaluate clustering structures based on the utility they present for our application. We validate the selected clusters in a use case where we successfully reconstruct customer archetypes previously developed by experts. Our approach shows promise for transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.
