Incomplete Data in Smart Grid: Treatment of Missing Values in Electric Vehicle Charging Data

In this paper, five imputation methods namely Constant (zero), Mean, Median, Maximum Likelihood, and Multiple Imputation methods have been applied to compensate for missing values in Electric Vehicle (EV) charging data. The outcome of each of these methods have been used as the input to a prediction algorithm to forecast the EV load in the next 24 hours at each individual outlet. The data is real world data at the outlet level from the University of California, Los Angeles (UCLA) campus parking lots. Given the sparsity of the data, both Median and Constant (=zero) imputations improved the prediction results. Since in most missing value cases in the database, all values of that instance are missing, the multivariate imputation methods did not improve the results significantly compared to univariate approaches.

• Record URL:
  https://doi.org/10.1109/ICCVE.2014.7297505
• Availability:
  • Find a library where document is available. Order URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7284675&punumber%3D7284675%26filter%3DAND%28p_IS_Number%3A7297490%29%26pageNumber%3D2%26rowsPerPage%3D100&pageNumber=3&rowsPerPage=100
• Supplemental Notes:
  • Copyright © 2014, IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Majidpour, Mostafa
  • Chu, Peter
  • Gadh, Rajit
  • Pota, Hemanshu R
• Conference:
  • 2014 International Conference on Connected Vehicles and Expo (ICCVE)
  • Location: Vienna , Austria
  • Date: 2014-11-3 to 2014-11-7
• Publication Date: 2015

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1041-1042
• Monograph Title: 2014 International Conference on Connected Vehicles and Expo (ICCVE)

Subject/Index Terms

• TRT Terms: Algorithms; Electric vehicle charging; Electric vehicles; Forecasting; Grids (Transmission lines); Parking lots
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01615678
• Record Type: Publication
• ISBN: 9781479967308
• Files: TRIS
• Created Date: Oct 31 2016 4:57PM