Resumen:
Non-intrusive load monitoring (NILM) systems have gained extensive interest due to their
potential role regarding power savings for residential customers. These systems, which are
mostly based on stages of detection and classification of transients on aggregated signals,
rely heavily on load signatures. In the literature, the image-based representations of voltagecurrent
(V-I) trajectories are claimed as the most effective individual steady-state signatures
for appliance classification. However, these representations inherit some drawbacks from
their generation process and they are thus incapable of inheriting all the information
encompassed by V-I trajectories. This work then proposes two steady-state appliance
signatures derived from the curvature scale space of V-I trajectories. These signatures aim to
improve the image representations of V-I trajectories by encompassing structural elements
related to the general shape of such trajectories as well as some characteristics neglected
during their generation. A group of load signatures formed from the proposed signatures
was evaluated on direct load classification and load disaggregation scenarios for four
publicly available datasets. The results achieved by the proposed representations surpassed
the sole employment of a reference image-based V-I signature for all the test scenarios
executed. Also, some of the evaluated signatures outperformed all known proposals that are
exclusively based on steady-state signatures for load classification on a given benchmark
dataset as well as on two other public datasets.
