Forecasting driving behavior to enable efficient grid integration of plug-in electric vehicles

In this paper, we evaluate a time series based method for predicting the first daily departure time (FDDT) of commuter vehicles. This task is relevant for the grid integration of plug-in electric vehicles (PEVs), since it allows for actively managing their electricity demand during the connection interval. Our study is based on a sample of 445 vehicle usage traces which were collected in the Puget Sound Traffic Choices Study using the Global Positioning System (GPS). We advance knowledge in the area of vehicle usage prediction in a smart grid context in four ways: First, we propose a method for selecting variable size subsets of vehicle usage traces that are more predictable irrespective of the concrete forecasting method used. Second, we show that FDDTs should not be modeled with basic. Third, we compare a number of time series based forecasting models using the mean absolute deviation (MAD) and forecast availability to identify a model with high forecasting accuracy and high forecast availability. Fourth, we report empirical confidence intervals to demonstrate the performance of our approach and discuss implications with respect to using PEVs for demand side management. A major finding of this work is that, even for commuters, FDDTs are hard to predict using historical realizations alone. However, the forecasting accuracy varies a lot from vehicle to vehicle and weekday to weekday. Moreover, forecasting errors are skewed in a way that facilitates non-disruptive charging control but may limit load shifting potential. Finally, using the example of ex-ante knowledge about workdays, we show that further potential for improvement lies in considering more data sources.