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To assess the performance of load disaggregation algorithms it is commonpractise to train a candidate algorithm on data from one or multiple householdsand subsequently apply cross-validation by evaluating the classification andenergy estimation performance on unseen portions of the dataset derived fromthe same households. With an emerging discussion of transferability inNon-Intrusive Load Monitoring (NILM), there is a need for domain-specificmetrics to assess the performance of NILM algorithms on new test scenariosbeing unseen buildings. In this paper, we discuss several metrics to assess thegeneralisation ability of NILM algorithms. These metrics target differentaspects of performance evaluation in NILM and are meant to complement thetraditional performance evaluation approach. We demonstrate how our metrics canbe utilised to evaluate NILM algorithms by means of two case studies. Weconduct our studies on several energy consumption datasets and take intoconsideration five state-of-the-art as well as four baseline NILM solutions.Finally, we formulate research challenges for future work.

On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring