Predicting EV parking behaviour in shared premises

Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.

Electrical vehicles  Energy management  Machine learning 

The global electric car sales continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises. The final objective is estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. We test the proposed approach in a combination of datasets from 2 different campus facilities in Italy and Brazil. The overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.

Source: BMDA 2023 - 5th International Workshop on Big Mobility Data Analytics co-located with EDBT/ICDT 2023 Joint Conference, Ioannina, Greece, 28/03/2023

Publisher: CEUR-WS.org, Aachen, DEU