Towards Predicting First Daily Departure Times: a Gaussian Modeling Approach for Load Shift Forecasting
This work addresses load shift forecasting in smart grids to manage electric vehicle disconnection times, but it is incremental as it builds on existing statistical models with specific dataset improvements.
The paper tackles the problem of predicting First Daily Departure Times (FDDTs) of electric vehicles for smart grid applications, proposing two Gaussian-based methods that achieve low error and high confidence (≈95%) for 15- and 10-minute intervals.
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 review of the relevant state-of-the-art driving behavior features towards FDDT prediction, to then propose an approximated Gaussian method which qualitatively forecasts how many vehicles will depart within a given time frame, by assuming that departure times follow a normal distribution. This method considers sampling sessions as Poisson distributions which are superimposed to obtain a single approximated Gaussian model. Given the Gaussian distribution assumption of the departure times, we also model the problem with Gaussian Mixture Models (GMM), in which the priorly set number of clusters represents the desired time granularity. Evaluation has proven that for the dataset tested, low error and high confidence ($\approx 95\%$) is possible for 15 and 10 minute intervals, and that GMM outperforms traditional modeling but is less generalizable across datasets, as it is a closer fit to the sampling data. Conclusively we discuss future possibilities and practical applications of the discussed model.