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Electric vehicle charging demand forecasting model based on big data technologies

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  • Arias, Mariz B.
  • Bae, Sungwoo
Abstract
This paper presents a forecasting model to estimate electric vehicle charging demand based on big data technologies. Most previous studies have not considered real-world traffic distribution data and weather conditions in predicting the electric vehicle charging demand. In this paper, the historical traffic data and weather data of South Korea were used to formulate the forecasting model. The forecasting processes include a cluster analysis to classify traffic patterns, a relational analysis to identify influential factors, and a decision tree to establish classification criteria. The considered variables in this study were the charging starting time determined by the real-world traffic patterns and the initial state-of-charge of a battery. Example case studies for electric vehicle charging demand during weekdays and weekends in summer and winter were presented to show the different charging load profiles of electric vehicles in the residential and commercial sites. The presented forecasting model may allow power system engineers to anticipate electric vehicle charging demand based on historical traffic data and weather data. Therefore, the proposed electric vehicle charging demand model can be the foundation for the research on the impact of charging electric vehicles on the power system.

Suggested Citation

  • Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:327-339
    DOI: 10.1016/j.apenergy.2016.08.080
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    References listed on IDEAS

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