Mining the energy consumption data of industrial systems to identify and characterize energy flexibility capabilities

Abstract

Industrial energy flexibility can play a pivotal supporting role in the transition towards renewable energy sources. Nonetheless, to harness the vast potential of industrial energy flexibility operation-friendly energy flexibility measures need to be identified and characterized. This work presents a step by step approach to mine historical energy consumption data of an industrial system using the k-means algorithm with support of the average silhouette score method to establish the system's typical operation profiles. These profiles can then be used not only to identify specific energy flexibility measures but their energy flexibility potential among other characterization parameters. The paper presents two representative use case examples and concludes by enumerating the benefits and providing an outlook of improvement opportunities for the developed approach.