Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

Inoussa Laouali, Antonio Ruano, Maria da Graça Ruano, Saad Dosse Bennani and Hakim El Fadili
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Inoussa Laouali: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Antonio Ruano: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Maria da Graça Ruano: DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
Saad Dosse Bennani: SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez P.O. Box 2202, Morocco
Hakim El Fadili: LIPI, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Bensouda, Fez P.O. Box 5206, Morocco

Energies, 2022, vol. 15, issue 3, 1-22

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 two models: a classification model 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.

Keywords: non-intrusive load monitoring; energy disaggregation; low frequency power data; convex hull; bidirectional long short time memory; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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