An Evaluation of NILM Approaches on Industrial Energy-Consumption Data

Abstract

Load disaggregation methods infer the energy consumption of individual appliances from their aggregated consumption. This facilitates energy savings and efficient energy management. However, most existing work on load disaggregation has only considered household settings. This may be due to companies preferring to not share their data, rendering such data hardly available. This article makes three contributions: First, we compare data describing the energy consumption of two facilities and of households. Second, we study the performance of seven prominent load disaggregation algorithms on industrial data and compare it to the one on household data. Our results indicate a performance gap on individual appliances. Third, we publish a tool converting an industrial data set to a standard format for load disaggregation, to facilitate further research and benchmarking in the field.