Low-frequency non-intrusive load monitoring of electric vehicles in houses with solar generation : generalisability and transferability

Tools

Vavouris, Apostolos and Garside, Benjamin and Stankovic, Lina and Stankovic, Vladimir (2022) Low-frequency non-intrusive load monitoring of electric vehicles in houses with solar generation : generalisability and transferability. Energies, 15 (6). 2200. ISSN 1996-1073 (https://doi.org/10.3390/en15062200)

[thumbnail of Vavouris-etal-Energies-2022-Low-frequency-non-intrusive-load-monitoring-of-electric-vehicles]
Preview
 Text. Filename: Vavouris_etal_Energies_2022_Low_frequency_non_intrusive_load_monitoring_of_electric_vehicles.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (500kB)| Preview

Abstract

Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-afternoon and early-evening hours, that can lead to great challenges that electricity grids need to face. Therefore, accurate estimation of Electric Vehicle (EV) charging loads and time of use is of utmost importance for different participants in the electricity markets. In this paper, a scalable methodology for detecting, from smart meter data, household EV charging events and their load consumption with robust evaluation, is proposed. This is achieved via a classifier based on Random Decision Forests (RF) with load reconstruction via novel post-processing and a regression approach based on sequence-to-subsequence Deep Neural Network (DNN) with conditional Generative Adversarial Network (GAN). Emphasis is placed on the generalisability of the approaches over similar houses and cross-domain transferability to different geographical regions and different EV charging profiles, as this is a requirement of any real-case scenario. Lastly, the effectiveness of different performance and generalisation loss metrics is discussed. Both the RF classifier with load reconstruction and the DNN, based on the sequence-to-subsequence model, can accurately estimate the energy consumption of EV charging events in unseen houses at scale solely from household aggregate smart meter measurements at 1–15 min resolutions.

ORCID iDs

Vavouris, Apostolos ORCID logoORCID: https://orcid.org/0000-0002-6138-7865, Garside, Benjamin, Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976 and Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420;
CORE (COnnecting REpositories)