  Choice of load signature or feature space is one of the most fundamental
design choices for non-intrusive load monitoring or energy disaggregation
problem. Electrical power quantities, harmonic load characteristics, canonical
transient and steady-state waveforms are some of the typical choices of load
signature or load signature basis for current research addressing appliance
classification and prediction. This paper expands and evaluates appliance load
signatures based on V-I trajectory - the mutual locus of instantaneous voltage
and current waveforms - for precision and robustness of prediction in
classification algorithms used to disaggregate residential overall energy use
and predict constituent appliance profiles. We also demonstrate the use of
variants of differential evolution as a novel strategy for selection of optimal
load models in context of energy disaggregation. A publicly available benchmark
dataset REDD is employed for evaluation purposes. Our experimental evaluations
indicate that these load signatures, in conjunction with a number of popular
classification algorithms, offer better or generally comparable overall
precision of prediction, robustness and reliability against dynamic, noisy and
highly similar load signatures with reference to electrical power quantities
and harmonic content. Herein, wave-shape features are found to be an effective
new basis of classification and prediction for semi-automated energy
disaggregation and monitoring.
