Deep learning-based forecasting of electric vehicle (EV) charging station availability
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 availabi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157989 |
| _version_ | 1826124214690643968 |
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| author | Lim, Lee Son |
| author2 | Su Rong |
| author_facet | Su Rong Lim, Lee Son |
| author_sort | Lim, Lee Son |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:17:01Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157989 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:17:01Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1579892023-07-07T19:13:17Z Deep learning-based forecasting of electric vehicle (EV) charging station availability Lim, Lee Son Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Engineering::Electrical and electronic engineering 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-26T12:24:14Z 2022-05-26T12:24:14Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/157989 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Lim, Lee Son Deep learning-based forecasting of electric vehicle (EV) charging station availability |
| title | Deep learning-based forecasting of electric vehicle (EV) charging station availability |
| title_full | Deep learning-based forecasting of electric vehicle (EV) charging station availability |
| title_fullStr | Deep learning-based forecasting of electric vehicle (EV) charging station availability |
| title_full_unstemmed | Deep learning-based forecasting of electric vehicle (EV) charging station availability |
| title_short | Deep learning-based forecasting of electric vehicle (EV) charging station availability |
| title_sort | deep learning based forecasting of electric vehicle ev charging station availability |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/157989 |
| work_keys_str_mv | AT limleeson deeplearningbasedforecastingofelectricvehicleevchargingstationavailability |