An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches

Silvia Moreno (), Hector Teran, Reynaldo Villarreal, Yolanda Vega-Sampayo, Jheifer Paez, Carlos Ochoa, Carlos Alejandro Espejo, Sindy Chamorro-Solano and Camilo Montoya
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Silvia Moreno: Centro de Investigación, Desarrollo Tecnológico e Innovación en Inteligencia Artificial y Robótica AudacIA, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Hector Teran: Faculty of Engineering, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Reynaldo Villarreal: Centro de Investigación, Desarrollo Tecnológico e Innovación en Inteligencia Artificial y Robótica AudacIA, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Yolanda Vega-Sampayo: Centro de Investigación, Desarrollo Tecnológico e Innovación en Inteligencia Artificial y Robótica AudacIA, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Jheifer Paez: Centro de Investigación, Desarrollo Tecnológico e Innovación en Inteligencia Artificial y Robótica AudacIA, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Carlos Ochoa: Faculty of Engineering, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Carlos Alejandro Espejo: Centro de Crecimiento Empresarial e Innovación Macondolab, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Sindy Chamorro-Solano: Centro de Investigación e Innovación en Biodiversidad y Cambio Climático Adaptia, Universidad Simón Bolívar, Barranquilla 080005, Colombia
Camilo Montoya: Solenium S.A.S., Medellin 050031, Colombia

Energies, 2024, vol. 17, issue 18, 1-11

Abstract: Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. However, optimizing power consumption through effective usage control and waste recycling also offers substantial potential for reducing energy demands. This study explores non-intrusive load monitoring (NILM) to estimate disaggregated energy consumption from a single household meter, leveraging advancements in deep learning such as convolutional neural networks. The study uses the UK-DALE dataset to extract and plot power consumption data from the main meter and identify five household appliances. Convolutional neural networks (CNNs) are trained with transfer learning using VGG16 and MobileNet. The models are validated, tested on split datasets, and combined using ensemble methods for improved performance. A new voting scheme for ensembles is proposed, named weighted average confidence voting (WeCV), and it is used to create combinations of the best 3 and 5 models and applied to NILM. The base models achieve up to 97% accuracy. The ensemble methods applying WeCV show an increased accuracy of 98%, surpassing previous state-of-the-art results. This study shows that CNNs with transfer learning effectively disaggregate household energy use, achieving high accuracy. Ensemble methods further improve performance, offering a promising approach for optimizing energy use and mitigating climate change.

Keywords: NILM; convolutional neural networks; climate change; energy consumption optimization (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: 2024
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