Abstract

Improving energy consumption feedback to end-users is crucial for energy optimization in buildings, thus reducing the impacts of energy waste on climate change. Non-intrusive Load Monitoring became a relevant approach for energy estimation in buildings. However, a more robust knowledge of the performance metrics used for evaluating the disaggregation algorithms is necessary for more accurate interpretation and conclusions. This work empirically analyses how the relationships between performance metrics vary across different appliance types and how they influence metric selection. Our results demonstrate that the kind of appliance disaggregated significantly influences metric relationships, providing critical insights for selecting an optimal set of metrics.