Short-term forecast of EV charging stations occupancy probability using
big data streaming analysis
- URL: http://arxiv.org/abs/2104.12503v1
- Date: Mon, 26 Apr 2021 12:03:02 GMT
- Title: Short-term forecast of EV charging stations occupancy probability using
big data streaming analysis
- Authors: Francesca Soldan, Enea Bionda, Giuseppe Mauri, Silvia Celaschi
- Abstract summary: This paper presents an architecture able to deal with data streams from a charging infrastructure.
The final aim is to forecast electric charging station availability after a set amount of minutes from present time.
The streaming model performs better than a model trained only using historical data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The widespread diffusion of electric mobility requires a contextual expansion
of the charging infrastructure. An extended collection and processing of
information regarding charging of electric vehicles may turn each electric
vehicle charging station into a valuable source of streaming data. Charging
point operators may profit from all these data for optimizing their operation
and planning activities. In such a scenario, big data and machine learning
techniques would allow valorizing real-time data coming from electric vehicle
charging stations. This paper presents an architecture able to deal with data
streams from a charging infrastructure, with the final aim to forecast electric
charging station availability after a set amount of minutes from present time.
Both batch data regarding past charges and real-time data streams are used to
train a streaming logistic regression model, to take into account recurrent
past situations and unexpected actual events. The streaming model performs
better than a model trained only using historical data. The results highlight
the importance of constantly updating the predictive model parameters in order
to adapt to changing conditions and always provide accurate forecasts.
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