DeepAI AI Chat
Log In Sign Up

A minimalist approach to scenario-based forecasting of electric vehicle consumption

by   Rahul Roy, et al.
University of Bath

Electrification of transport is a key strategy in reducing carbon emissions. Many countries have adopted policies of complete but gradual transformation to electric vehicles (EVs). However, mass EV adoption also means a spike in electricity demand, which in turn can disrupt existing electricity infrastructure. Good EV consumption forecasts are key for distribution network operators (DNOs) to effectively manage demand and capacity. In this paper, we consider a suite of models to forecast EV consumption. More specifically, we evaluate a nested modeling approach for scenario-based forecasting of EV consumption. Using the data collected as part of the Electric Nation trials, we studied statistical models (Time Series Regression and Regression with ARIMA Errors), scalable machine learning systems (Extreme Gradient Boosting or XGB), and artificial neural networks (Long Short-Term Memory Networks or LSTMs). We found that LSTMs delivered the best forecasting performance.


page 1

page 9

page 10

page 13


Short-term CO2 emissions forecasting based on decomposition approaches

We are facing major challenges related to global warming and emissions o...

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Modern energy systems collect high volumes of data that can provide valu...

A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads

As a powerful tool to improve their efficiency and sustainability, most ...

Short-term forecast of EV charging stations occupancy probability using big data streaming analysis

The widespread diffusion of electric mobility requires a contextual expa...

A Clustering Framework for Residential Electric Demand Profiles

The availability of residential electric demand profiles data, enabled b...

Parallel Statistical Model Checking for Safety Verification in Smart Grids

By using small computing devices deployed at user premises, Autonomous D...