Feedback-driven real-time forecasting method for the arrival times of electric vehicles

Wu, Chuanshen, Han, Haiteng and Gao, Shan 2024. Feedback-driven real-time forecasting method for the arrival times of electric vehicles. Electric Power Systems Research 228 , 110077. 10.1016/j.epsr.2023.110077 Item availability restricted.

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0378-7796
Date of First Compliant Deposit:	24 January 2024
Date of Acceptance:	11 December 2023
Last Modified:	25 Jan 2024 10:36
URI:	https://orca.cardiff.ac.uk/id/eprint/165537

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