… learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks.
… prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction …

… • We evaluate the proposed demand prediction approach on both real and simulated data …
from over 3000 stations and 16,000 electric vehicles in operation. Extensive experiments have …

… regression framework for the charging demand prediction at candidate sites is presented. …
-specific information which may be crucial to predicting the output variable. To address this …

This paper presents a time-spatial electric vehicle (EV) charging-power demand forecast
model at fast-charging stations located in urban areas. Most previous studies have considered …

… propose a novel dynamic demand prediction approach for expanding electric vehicle (EV) …
process, and is able to robustly predict demand for both existing stations and the planned …

… records of 25,489 electric passenger vehicles in Beijing are … of electric vehicles and support
charging demand prediction. … with >60 electric vehicle behavior characteristic parameters …

… propose a novel demand prediction approach for electric vehicle (EV) sharing systems,
which learns the complex system dynamics, and is able to robustly predict demand for stations. …

… Short-term demand prediction … demand prediction refers to monthly or yearly forecast of
the charging stations within the entire region. The main purpose of long-term demand prediction …

… electric vehicle charge demand prediction refers to predicting current or future charging demand
… In this paper, the prediction is for a group of stations and the overall charge of the system…

… The results depicted in Table I indicate that k-Nearest Neighbors and Random Forest
outperform other ML methods for Session Duration and Energy Demand prediction, respectively. …