Abstract
Affected by weather conditions, traffic conditions, and driver behavior, the arrival characteristics of electric vehicles (EVs) vary significantly from day to day. This study proposes a feedback-driven real-time forecasting approach that combines historical data to improve the forecasting accuracy of arrival times of EVs. For model-based forecasting methods that sample from probability density functions (PDFs), the related parameter values are dynamically optimized. Compared with sampling from PDFs with empirical parameter values, the dynamic optimal parameter values can track the characteristics of EV arrivals by fully using the continuously updated EV feedback. Considering robustness, a historical data-based support vector clustering technology is utilized to obtain the optimization range of optimal parameter values. As a key of this study, the conservativeness of the optimization range is dynamically adjusted with the periodic updates of EV feedback. The experimental results indicate that, by making full utilization of EV feedback, the proposed method can effectively reduce the forecasting errors of EV arrival times.
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