With increasing digitization, new opportunities emerge concerning the availability and use of data in the energy sector. A comprehensive literature review shows an abundance in available unsupervised clustering algorithms as well as internal, relative and external cluster validation indices (cvi) to evaluate the results. Yet, the comparison of different clustering results on the same dataset, executed with different algorithms and a specific practical goal in mind still proves scientifically challenging. A large variety of cvi are described and consolidated in commonly used composite indices (e.g. Davies-Bouldin-Index, silhouette-Index, Dunn-Index). Previous works show the challenges surrounding these composite indices since they serve a generalized cluster quality evaluation. However, this does not suit individual clustering goals in many cases. The presented paper introduces the current state of science, existing cluster validation indices and proposes a practical method to combine them to an individual composite index, using Multi Criteria Decision Analysis (mcda). The methodology is applied on two energy economic use cases for clustering load profiles of bidirectional electric vehicles and municipalities.

中文翻译：

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能源部门集群验证的实用方法

随着数字化程度的提高，能源部门数据的可用性和使用出现了新的机遇。全面的文献综述显示了大量可用的无监督聚类算法以及内部、相对和外部聚类验证指数 (cvi) 来评估结果。然而，在同一数据集上比较不同的聚类结果，使用不同的算法和特定的实际目标执行，仍然证明在科学上具有挑战性。在常用的复合指数（例如 Davies-Bouldin-Index、silhouette-Index、Dunn-Index）中描述和合并了大量的 CVI。以前的工作显示了围绕这些复合指数的挑战，因为它们用于广义的集群质量评估。然而，这在许多情况下并不适合单独的聚类目标。本文介绍了科学的当前状态、现有的集群验证指数，并提出了一种实用的方法，使用多标准决策分析 (mcda) 将它们组合到单个复合指数中。该方法应用于两个能源经济用例，用于对双向电动汽车和市政当局的负载曲线进行聚类。