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

Conference · Thu Jul 31 00:00:00 EDT 2014

DOI:https://doi.org/10.1109/PESGM.2014.6938864· OSTI ID:1332693

Majidpour, Mostafa; Qiu, Charlie; Chung, Ching-Yen; Chu, Peter; Gadh, Rajit; Pota, Hemanshu R.

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.

• Research Organization: City of Los Angeles Department
• Sponsoring Organization: USDOE Office of Electricity (OE)
• DOE Contract Number: OE0000192
• OSTI ID: 1332693
• Report Number(s): DOE-UCLA-00192-47
• Resource Relation: Conference: 2014 IEEE PES General Meeting National Harbor, MD, USA. 27-31 July 2014
• Country of Publication: United States
• Language: English