Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/157989
Title: | Deep learning-based forecasting of electric vehicle (EV) charging station availability | Authors: | Lim, Lee Son | Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2022 | Publisher: | Nanyang Technological University | Source: | Lim, L. S. (2022). Deep learning-based forecasting of electric vehicle (EV) charging station availability. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157989 | Abstract: | In the modern urban intelligent transportation system, high accuracy prediction of the public transportation facilities usage condition can help drivers to arrange daily commute wisely. This project focuses on applying the advanced deep learning algorithm to forecast the EV charging station availability in one real world case. Related baseline methods will be also executed to compare the prediction performance across different horizons. By the end of this project, it is expected to develop the AI system to grasp the periodic behavior of charging and predict the long-term EV charging station availability with high accuracy. Spatial-Temporal Network based algorithm and Attention Mechanism based algorithm are good options. | URI: | https://hdl.handle.net/10356/157989 | Schools: | School of Electrical and Electronic Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FYP Final Report.pdf Restricted Access | FYP final report | 1.98 MB | Adobe PDF | View/Open |
Page view(s)
49
Updated on Sep 30, 2023
Download(s)
10
Updated on Sep 30, 2023
Google ScholarTM
Check
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.