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Capturing heterogeneity in electricity distribution operations: A critical review of latent class modelling

机译：捕捉配电运营中的异质性：潜在类建模的重要回顾

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摘要

Recently, several articles (Cullmann, 2012; Agrell et al., 2014; Filippini and Orea, 2014; Llorca et al., 2014) address the issue of benchmarking decision making units with different technologies by using latent class models. This method groups units that have similar technology for better comparison. Under this scheme, there are two implicit assumptions: First, that each class reflects a unique technology where its elements are not outliers. Second, classes are assumed to be stationary and fixed. If this assumption is violated, the classification is transient and time-dependent, inadequate for the regulatory use suggested in the seminal papers. We apply latent class models to classify Swedish electricity distributors under different specifications. In most of the models, we identify one large class with approximately 78.4% of the DMU's and two small classes with 7.4% and 14.2% respectively. Moreover, most of small classes elements switch between categories. We contrast our parametric results with nonparametric outlier detector methods and find a relationship between identified outliers and the elements of smaller residual classes. We believe that our work is an important caveat to the adoption of latent class modelling as an alternative or remedy for conventional models, relying on a homogeneous reference set.
机译：最近，有几篇文章（Cullmann，2012； Agrell等，2014； Filippini和Orea，2014； Llorca等，2014）通过使用潜在类模型解决了使用不同技术对决策单位进行基准测试的问题。此方法将具有相似技术的单元分组以进行更好的比较。在此方案下，有两个隐含的假设：首先，每个类别都反映了一种独特的技术，其要素并不孤立。其次，假定类是固定的和固定的。如果违反了此假设，则分类是短暂的且与时间有关，不足以影响精打细算的建议。我们使用潜在类别模型对不同规格下的瑞典配电商进行分类。在大多数模型中，我们确定一个大类，分别占DMU的78.4％，两个小类，分别占7.4％和14.2％。而且，大多数小类元素在类别之间切换。我们将参数结果与非参数离群值检测器方法进行对比，并找到已识别的离群值与较小残差类别的元素之间的关系。我们相信，我们的工作是对潜在类建模的依赖，这是对传统模型的替代或补救方法的重要告诫，因为它们依赖于同类参考集。

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