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https://hdl.handle.net/20.500.12008/36564
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Title: | NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives |
Authors: | Mariño, Camilo Cossio, Guillermo Massaferro Saquieres, Pablo Di Martino, Matías Gómez, Alvaro Fernández, Alicia |
Type: | Ponencia |
Keywords: | NILM, Electric vehicles, Load disaggregation, Deep learning, Renewable energy sources, Power demand, Machine learning algorithms, Neural networks, Water heating, Electric vehicles |
Issue Date: | 2023 |
Abstract: | Due to its impact on household energy use and the adoption of renewable energies, the intelligent management of the power consumption of electric vehicles (EVs) is of great relevance. In the context of widespread clean energy adoption and growing environmental concerns, generating incentives through discounted rates for intelligent residential EV power consumption requires algorithms capable of measuring loads in a disaggregated manner. The deployment of smart meter networks offers the possibility of applying machine learning techniques to estimate EV residential consumption. This work presents an efficient algorithm for the Non Intrusive Load Monitoring (NILM) of EV consumption, which is an adaptation of a method previously proposed for high-powered water heaters. Its performance is compared with methods based on deep neural networks. Results from an actual power demand dataset are discussed, and a comparative analysis is carried out against billing rules based on time slots and historical power consumption data. |
Publisher: | IEEE |
IN: | 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan, pp 1-5 |
Sponsors: | Beca Maestría CAP Camilo Mariño Proyecto bajo financiación convenio UTE |
Citation: | Mariño, C, Cossio, G, Massaferro Saquieres, P. y otros. NILMEV : Electric Vehicle disaggregation for residential customer energy efficiency incentives [en línea]. EN: 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan 2023, pp 1-5. DOI: 10.1109/ISGT51731.2023.10066441 |
Geographic coverage: | Uruguay Costa Rica |
Academic department: | Procesamiento de Señales |
Investigation group: | Tratamiento de Imágenes |
License: | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
Appears in Collections: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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File | Description | Size | Format | ||
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MCMDGF23.pdf | Versión final | 315,19 kB | Adobe PDF | View/Open |
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