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| ■論文No. |
CMN24027 |
| ■ページ数 |
6ページ |
■発行日
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2024/03/25 |
| ■タイトル |
Prediction of EV and Non-EV Vehicles by Leveraging Transaction Data for Charging Station Site Optimization |
| ■タイトル(英語) |
Prediction of EV and Non-EV Vehicles by Leveraging Transaction Data for Charging Station Site Optimization |
| ■著者名 |
Telang Geet Tapan(Hitachi India Private Limited),Vellandurai Akhash(Hitachi India Private Limited),Sharma Ankit(Hitachi India Private Limited),Kumar Vinoth(Hitachi India Private Limited) |
| ■著者名(英語) |
Geet Tapan Telang(Hitachi India Private Limited),Akhash Vellandurai(Hitachi India Private Limited),Ankit Sharma(Hitachi India Private Limited),Vinoth Kumar(Hitachi India Private Limited) |
| ■価格 |
会員 ¥220 一般 ¥330 |
| ■書籍種類 |
研究会(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 通信研究会 |
| ■本誌 |
2024年3月28日-2024年3月29日通信研究会
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| ■本誌掲載ページ |
55-60ページ |
| ■原稿種別 |
英語 |
| ■電子版へのリンク |
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| ■キーワード |
EV Classification|Payments |EV Charger|Optimization|EV Classification|Payments |EV Charger|Optimization |
| ■要約(日本語) |
The proposed research paper introduces a novel methodology for predicting Electric Vehicle (EV) demand by differentiating EV and non-EV customers using merchant transaction data. Addressing the urgent need for more EV Charging Stations and existing challenge of identifying optimal EV Charging Station placement due to the unavailability of direct EV demand data, the method employs estimates of electric vehicle demand by integrating geospatial data with merchant financial transactions. A predictive model using Deep Neural Networks (DNN) is developed, achieving a 94% accuracy rate in identifying EV user frequency at merchant sites thereby facilitating strategic placement of EV Charging Stations. |
| ■要約(英語) |
The proposed research paper introduces a novel methodology for predicting Electric Vehicle (EV) demand by differentiating EV and non-EV customers using merchant transaction data. Addressing the urgent need for more EV Charging Stations and existing challenge of identifying optimal EV Charging Station placement due to the unavailability of direct EV demand data, the method employs estimates of electric vehicle demand by integrating geospatial data with merchant financial transactions. A predictive model using Deep Neural Networks (DNN) is developed, achieving a 94% accuracy rate in identifying EV user frequency at merchant sites thereby facilitating strategic placement of EV Charging Stations. |
| ■版 型 |
A4 |
| ■PDFファイルサイズ |
1,173Kバイト |
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