An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring

Abstract

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.

Automated classification of appliances using elliptical fourier descriptors

“…It is then possible to calculate features such as the harmonics [9] and frequency components [10] from the steadystate and transient behavior of the current and voltage signal. More recently, the possibility to consider voltage-current (VI) trajectories has also been considered [11], [12], [13], [14].…”

Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach

“…Type I presents on-ff appliances having two states, type II describes FSM devices having multiple operation states, and type III stands for continuously variable loads [37,38]. The most energy-intensive home appliances are finite-state loads, which can be decomposed as a combination of elementary profiles of type I appliances.…”

“…A low-frequency data sampling rate can be executed using existing metering infrastructure without additional installation, whilst high-frequency data acquisition can provide more information and assist in a more accurate and precise analysis. However, this method undergoes difficulties, for example, high set-up costs and a more complex signature database to manage [23,24]. Features based on a high-frequency sampling rate cannot be considered for analyzing linear resistive loads such as ESHs [14,25] because of the similarity between voltage and current waveforms.…”

Non-intrusive load monitoring under residential solar power influx

“…Due to its both low cost and complexity, numerous NILM methods have been proposed in the literature [11,[17][18][19][20][21][22][23][24]. These methods enable the identification of turned ON appliances inside a customer premises with their respective power levels non-intrusively.…”