Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier

TL;DR

This paper introduces a novel non-intrusive appliance identification method using local power histogramming and an improved KNN classifier, achieving high accuracy across multiple datasets for efficient energy monitoring.

Contribution

The paper presents a new 2D local power histogram descriptor combined with an improved KNN algorithm for more accurate and faster appliance identification in NILM systems.

Findings

Achieved up to 99.65% accuracy on GREEND dataset.

Demonstrated high accuracy (>96%) on multiple datasets.

Enhanced discrimination between different appliance categories.

Abstract

Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power histogramming (LPH) descriptor, in which appliance power signals are transformed into 2D space and short histograms are extracted to represent each device. Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. This results in highly improving the discrimination ability between appliances belonging to distinct categories. A deep evaluation of the proposed LPH-IKNN based solution is investigated under different data sets, in which the proposed scheme leads to promising performance. An accuracy of up to 99.65% and 98.51% has been achieved on GREEND and UK-DALE data sets, respectively. While an accuracy of more than 96% has been attained on both WHITED and PLAID data sets. This proves the validity of using 2D descriptors to accurately identify appliances and create new perspectives for the NILM problem.