Electric Vehicle Driver Clustering using Statistical Model and Machine Learning
This work addresses the challenge of managing EV charging uncertainties for distribution energy systems, but it is incremental as it applies existing methods to new real-world data.
The paper tackled the problem of predicting electric vehicle (EV) load for smart energy management by developing a model that combines statistical analysis and machine learning, achieving good performance in day-ahead parking and load prediction as shown by cross-validation results.
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.