  Electric Vehicle (EV) is playing a significant role in the distribution
energy management systems since the power consumption level of the EVs is much
higher than the other regular home appliances. The randomness of the EV driver
behaviors make the optimal charging or discharging scheduling even more
difficult due to the uncertain charging session parameters. To minimize the
impact of behavioral uncertainties, it is critical to develop effective methods
to predict EV load for smart EV energy management. Using the EV smart charging
infrastructures on UCLA campus and city of Santa Monica as testbeds, we have
collected real-world datasets of EV charging behaviors, based on which we
proposed an EV user modeling technique which combines statistical analysis and
machine learning approaches. Specifically, unsupervised clustering algorithm,
and multilayer perceptron are applied to historical charging record to make the
day-ahead EV parking and load prediction. Experimental results with
cross-validation show that our model can achieve good performance for charging
control scheduling and online EV load forecasting.
