Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data
Abstract
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 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 our database, all values of that instance are missing, the multivariate imputation methods did not improve the results significantly compared to univariate approaches.
- Authors:
- Publication Date:
- Research Org.:
- City of Los Angeles Department
- Sponsoring Org.:
- USDOE Office of Electricity (OE)
- OSTI Identifier:
- 1332711
- Report Number(s):
- DOE-UCLA-00192-22
- DOE Contract Number:
- OE0000192
- Resource Type:
- Conference
- Resource Relation:
- Conference: 2014 International Conference on Connected Vehicles and Expo (ICCVE) Vienna, Austria 3-7 Nov. 2014
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Majipour, Mostafa, Chu, Peter, Gadh, Rajit, and Pota, Hemanshu R. Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data. United States: N. p., 2014.
Web.
Majipour, Mostafa, Chu, Peter, Gadh, Rajit, & Pota, Hemanshu R. Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data. United States.
Majipour, Mostafa, Chu, Peter, Gadh, Rajit, and Pota, Hemanshu R. 2014.
"Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data". United States. https://www.osti.gov/servlets/purl/1332711.
@article{osti_1332711,
title = {Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data},
author = {Majipour, Mostafa and Chu, Peter and Gadh, Rajit and Pota, Hemanshu R.},
abstractNote = {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 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 our database, all values of that instance are missing, the multivariate imputation methods did not improve the results significantly compared to univariate approaches.},
doi = {},
url = {https://www.osti.gov/biblio/1332711},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Nov 03 00:00:00 EST 2014},
month = {Mon Nov 03 00:00:00 EST 2014}
}