Abstract
Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications – device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.
Highlights
In the era of modern and revolutionized power system networks, most electric utilities tend to upgrade the conventional grid architectures to more advanced, technically prudent, selfhealing, better controlled and optimized smart grid technolo-The associate editor coordinating the review of this manuscript and approving it for publication was Mauro Gaggero .gies [1]
It has been shown that Non-intrusive load monitoring (NILM) applies a single-point measurement tool, which ratifies inexpensive and smart metering structure of electrical appliances deployed in real life consumption
Capsule network based device classification system consisted of convolutional neural network (CNN) as the feature descriptor has been proposed and implemented in this article
Summary
In the era of modern and revolutionized power system networks, most electric utilities tend to upgrade the conventional grid architectures to more advanced, technically prudent, selfhealing, better controlled and optimized smart grid technolo-. Microscopic characteristics collected from current and voltage measurements, phase noise of individual device as a new load signature characteristic and a technique comprising linear-chain conditional random fields (CRFs) are reported in [15], [16] and [17], respectively Another effective NILM solution based on gated linear unit convolutional layers is proposed in [18]. A statistical features based NILM solution is proposed in [28], which applies a number of supervised classifiers on current measurements and evaluates performance comparison for device disaggregation Another statistical features extraction approach for NILM application is reported in [29], wherein current envelopes are determined from normalized recordings by analyzing the transient behavior of the device operations and the most prominent feature components are extracted to train and test a multi-class classification model.
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