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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12008/36564 How to cite
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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