A practical approach to cluster validation in the energy sector

Alexander Bogensperger,Yann Fabel

doi:10.1186/s42162-021-00177-1

Abstract

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.

Highlights

With increasing amounts of data in the energy sector, the relevance of data analysis is increasing constantly
The literature review shows a wide variety of available clustering algorithms
Most realm-specific papers provide little to no explanation on their cvi choice or choice in clustering algorithm(s)

Summary

Introduction

With increasing amounts of data in the energy sector, the relevance of data analysis is increasing constantly. This is mainly caused by the rising numbers of smart meters and decentralized energy resources (DER) as well as sensors and actors in infrastructures and new assets (i.e., through sector coupling). This trend is causing a growing complexity in handling incoming data, purposefully utilizing it and managing the complexity of the system. This paper focuses on the utilization of data with a given goal in mind. In the early stages of the digitization of the energy industry, with newly available data

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Discussion

Conclusion