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Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques thatprovide insights into the energy consumption of households and industrialfacilities. Latest contributions show significant improvements in terms ofaccuracy and generalisation abilities. Despite all progress made concerningdisaggregation techniques, performance evaluation and comparability remains anopen research question. The lack of standardisation and consensus on evaluationprocedures makes reproducibility and comparability extremely difficult. In thispaper, we draw attention to comparability in NILM with a focus on highlightingthe considerable differences amongst common energy datasets used to test theperformance of algorithms. We divide discussion on comparability into dataaspects, performance metrics, and give a close view on evaluation processes.Detailed information on pre-processing as well as data cleaning methods, theimportance of unified performance reporting, and the need for complexitymeasures in load disaggregation are found to be the most urgent issues inNILM-related research. In addition, our evaluation suggests that datasetsshould be chosen carefully. We conclude by formulating suggestions for futurework to enhance comparability.

Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation