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Title: Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection
Authors: Laouali, Inoussa
Ruano, Antonio
Ruano, Maria da Graça 
Bennani, Saad Dosse
Fadili, Hakim El
Keywords: non-intrusive load monitoring; energy disaggregation; low frequency power data; convex hull; bidirectional long short time memory; convolutional neural networks
Issue Date: 2022
Project: Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020 
FCT - UID/EMS/50022/2020 
Serial title, monograph or event: Energies
Volume: 15
Issue: 3
Abstract: The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of twomodels: a classificationmodel based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.
ISSN: 1996-1073
DOI: 10.3390/en15031215
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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