Incomplete Data in Smart Grid: Treatment of 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 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.
- Research Organization:
- City of Los Angeles Department
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- OE0000192
- OSTI ID:
- 1332711
- Report Number(s):
- DOE-UCLA-00192-22
- 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
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