Decomposition-Transformation Assisted Optimized Heterogeneous Classification Strategy in NILM

Accurate identification of various loads for nonintrusive load monitoring (NILM) plays a crucial role in a variety of power system applications and strategies. In this article, a novel approach is proposed for performing load identification which consists of three main parts: features extraction considering decomposition-transformation strategy, appropriate feature screening, and employing a suitable classification strategy for performance improvement. The proposed approach first decomposes a load current into multiple modes by utilizing an adaptive signal decomposition technique, namely, variational mode decomposition (VMD), followed by the determination of the auto-correlograms of the modes. Multiple features are extracted from the obtained modes and their respective auto-correlograms. Postfeature extraction, combinations of multiple feature ranking (FR) techniques via union and intersection strategies are explored in contrast to employing only a single FR technique as prevalent in existing literature in NILM. Finally, considering the selected features by above stated FR combinations, a heterogeneous classifier strategy is adopted for performing classification that outperforms a single classifier as well as random forest classifiers on account of its heterogeneous structure. For further performance improvement, a surrogate-based optimization technique is utilized for determining the appropriate hyperparameter values in contrast to manual tuning which is prominent in existing works. Experimental appliance data obtained from publicly available datasets and execution via the software-in-loop mode have been considered to put forward the efficacy of the proposed approach.