Journal ArticleOpen Access
Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network
Authors
Author Affiliations
Bangladesh University of Engineering and Technology, University of South Carolina, Oregon Institute of Technology
Published InIEEE Access
Year2020
Citations22
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…
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Fields & Keywords
Physical SciencesEngineeringElectrical and Electronic EngineeringSmart Grid Energy ManagementEnergy Load and Power ForecastingEnergy Efficiency and ManagementData miningReal-time computingArtificial intelligenceMachine learningDatabaseProgramming languageElectrical engineeringMechanical engineeringStatisticsGeometry