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Title: Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application

Abstract

This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, kNearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for the cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
City of Los Angeles Department
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1332693
Report Number(s):
DOE-UCLA-00192-47
DOE Contract Number:
OE0000192
Resource Type:
Conference
Resource Relation:
Conference: 2014 IEEE PES General Meeting National Harbor, MD, USA. 27-31 July 2014
Country of Publication:
United States
Language:
English

Citation Formats

Majidpour, Mostafa, Qiu, Charlie, Chung, Ching-Yen, Chu, Peter, Gadh, Rajit, and Pota, Hemanshu R. Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application. United States: N. p., 2014. Web. doi:10.1109/PESGM.2014.6938864.
Majidpour, Mostafa, Qiu, Charlie, Chung, Ching-Yen, Chu, Peter, Gadh, Rajit, & Pota, Hemanshu R. Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application. United States. doi:10.1109/PESGM.2014.6938864.
Majidpour, Mostafa, Qiu, Charlie, Chung, Ching-Yen, Chu, Peter, Gadh, Rajit, and Pota, Hemanshu R. Thu . "Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application". United States. doi:10.1109/PESGM.2014.6938864. https://www.osti.gov/servlets/purl/1332693.
@article{osti_1332693,
title = {Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application},
author = {Majidpour, Mostafa and Qiu, Charlie and Chung, Ching-Yen and Chu, Peter and Gadh, Rajit and Pota, Hemanshu R.},
abstractNote = {This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, kNearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for the cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.},
doi = {10.1109/PESGM.2014.6938864},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jul 31 00:00:00 EDT 2014},
month = {Thu Jul 31 00:00:00 EDT 2014}
}

Conference:
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