Semisupervised Multilabel Deep Learning Based Nonintrusive Load Monitoring in Smart Grids

Publication Title

IEEE Transactions on Industrial Informatics

ISSN

15513203

E-ISSN

19410050

Document Type

Article

Abstract

Nonintrusive load monitoring (NILM) is a technique that infers appliance-level energy consumption patterns and operation state changes based on feeder power signals. With the availability of fine-grained electric load profiles, there has been increasing interest in using this approach for demand-side energy management in smart grids. NILM is a multilabel classification problem due to the simultaneous operation of multiple appliances. Recently, deep learning based techniques have been shown to be a promising approach to solving this problem, but annotating the huge volume of load profile data with multiple active appliances for learning is very challenging and impractical. In this article, a new semisupervised multilabel deep learning based framework is proposed to address this problem with the goal of mitigating the reliance on large labeled datasets. Specifically, a temporal convolutional neural network is used to automatically extract high-level load signatures for individual appliances. These signatures can be efficiently used to improve the feature representation capability of the framework. Case studies conducted on two open-access NILM datasets demonstrate the effectiveness and superiority of the proposed approach.

First Page

6892

Last Page

6902

Publication Date

11-1-2020

DOI

10.1109/TII.2019.2955470

Keywords

Deep learning, load signatures, multilabel classification, nonintrusive load monitoring (NILM), semisupervised learning (SSL)

Department

Department of Electrical Engineering

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