Towards Predicting First Daily Departure Times: a Gaussian Modeling Approach for Load Shift Forecasting

Abstract

This work provides two statistical Gaussian forecasting methods for predicting First Daily Departure Times (FDDTs) of everyday use electric vehicles. This is important in smart grid applications to understand disconnection times of such mobile storage units, for instance to forecast storage of non dispatchable loads (e.g. wind and solar power). We provide a… (More)

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Cite this paper

@article{Kirk2015TowardsPF, title={Towards Predicting First Daily Departure Times: a Gaussian Modeling Approach for Load Shift Forecasting}, author={Nicholas H. Kirk and Ilya Dianov}, journal={CoRR}, year={2015}, volume={abs/1507.04502} }