Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability
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- Zhao, Bochao & Ye, Minxiang & Stankovic, Lina & Stankovic, Vladimir, 2020. "Non-intrusive load disaggregation solutions for very low-rate smart meter data," Applied Energy, Elsevier, vol. 268(C).
- Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
- Thamer Alquthami & Abdullah Alsubaie & Mohannad Alkhraijah & Khalid Alqahtani & Saad Alshahrani & Murad Anwar, 2022. "Investigating the Impact of Electric Vehicles Demand on the Distribution Network," Energies, MDPI, vol. 15(3), pages 1-18, February.
- Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
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- Vavouris, Apostolos & Guasselli, Fernanda & Stankovic, Lina & Stankovic, Vladimir & Gram-Hanssen, Kirsten & Didierjean, Sébastien, 2024. "A complex mixed-methods data-driven energy-centric evaluation of net-positive households," Applied Energy, Elsevier, vol. 367(C).
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Keywords
non-intrusive load monitoring (NILM); energy dissagregation; electric vehicles (EVs); deep neural networks; transfer learning;All these keywords.
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