Ensemble machine learning-based algorithm for electric vehicle user behavior prediction

• Published in: Applied energy Vol. 254; p. 113732
• Main Authors: Chung, Yu-Wei, Khaki, Behnam, Li, Tianyi, Chu, Chicheng, Gadh, Rajit
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2019
• Subjects: Data entropy; Data sparsity; EV user behavior prediction; Kernel density estimator; Machcine learning; Machcine learning; Kernel density estimator; Data sparsity; Data entropy; EV user behavior prediction
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.113732

[Display omitted] •Real electric vehicle charging data from 252 users were analyzed.•Defining the data entropy/sparsity ratio (R) as an indicator for predicting algorithm selection.•Exploiting the benefit of using diffusion-based kernel density estimator (DKDE) for prediction with high R data.•Reducing at least 10% of prediction error compared to a single predicting algorithm. This research investigates electric vehicle (EV) charging behavior and aims to find the best method for its prediction in order to optimize the EV charging schedule. This paper discusses several commonly used machine learning algorithms to predict charging behavior, including stay duration and energy consumption based on historical charging records. It is noted that prediction error increases along with the rise of data entropy or the decrease of data sparsity. Thus, this paper accounts for both indicators by defining the entropy/sparsity ratio (R). When R is low, support vector regression (SVR) and random forest (RF) regression show better accuracy for stay duration and energy consumption predictions, respectively. While R is high, a diffusion-based kernel density estimator (DKDE) performs better for both predictions. The three methods are assembled as the proposed Ensemble Predicting Algorithm (EPA) to improve predicting performance by decreasing 11% of the duration and 22% of the energy consumption prediction errors. The prediction results are then applied to an optimal EV charging scheduling algorithm to minimize load variance while reducing the EV charging cost. A numerical simulation using real charging data is conducted to show the effectiveness of improved predictions and EV load management. The results show that the charging scheduling combined with EPA prediction can reduce 27% of peak load, 10% of load variation, and 4% cost reduction, compared to uncoordinated charging.