Efficient multi-descriptor fusion for non-intrusive appliance recognition

TL;DR

This paper introduces a novel multi-descriptor fusion method using ensemble bagging trees for non-intrusive appliance recognition, significantly improving accuracy by combining time-domain features and testing on diverse datasets.

Contribution

The paper proposes a new feature fusion technique (fTDF) and a classifier design that enhances appliance recognition accuracy over existing methods.

Findings

fTDF improves feature discrimination and recognition accuracy

The method outperforms other TD descriptors and classifiers

Effective on datasets with different sampling frequencies

Abstract

Consciousness about power consumption at the appliance level can assist user in promoting energy efficiency in households. In this paper, a superior non-intrusive appliance recognition method that can provide particular consumption footprints of each appliance is proposed. Electrical devices are well recognized by the combination of different descriptors via the following steps: (a) investigating the applicability along with performance comparability of several time-domain (TD) feature extraction schemes; (b) exploring their complementary features; and (c) making use of a new design of the ensemble bagging tree (EBT) classifier. Consequently, a powerful feature extraction technique based on the fusion of TD features is proposed, namely fTDF, aimed at improving the feature discrimination ability and optimizing the recognition task. An extensive experimental performance assessment is performed on two different datasets called the GREEND and WITHED, where power consumption signatures were gathered at 1 Hz and 44000 Hz sampling frequencies, respectively. The obtained results revealed prime efficiency of the proposed fTDF based EBT system in comparison with other TD descriptors and machine learning classifiers.