Art

J-GLOBAL ID：201902254309851506   Reference number：19A1278006

EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions

教師付き機械学習回帰を比較する充電インフラストラクチャーに関するEVアイドル時間推定【JST・京大機械翻訳】

Author (3)： Lucas Alexandre (European Commission, Joint Research Centre (JRC), Directorate C-Energy, Transport and Climate, Via E. Fermi 2749, I-21027 Ispra (VA), Italy) ,  Barranco Ricardo (European Commission, Joint Research Centre (JRC), Directorate B-Growth and Innovation, Via E. Fermi 2749, I-21027 Ispra (VA), Italy) ,  Refa Nazir (ElaadNL, Utrechtseweg 310 (bld. 42B), 6812 AR Arnhem (GL), The Netherlands)
Material： Energies (Web)
Volume： 12  Issue： 2  Page： 269  Publication year： 2019 
JST Material Number： U7016A  ISSN： 1996-1073  CODEN： ENERGA  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： The adoption of electric vehic...

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Thesaurus term： management ,  *infrastructure ,  parameter ,  availability ,  *machine learning ,  electric power ,  electric automobile ,  network ,  *idle time ,  *charge(battery) ,  algorithm ,  city ,  Kingdom of the Netherlands
Semi thesaurus term： 所有者 ,  公共政策 , 【Automatic Indexing@JST】

Author keywords (7)： charging infrastructure ,  idle time ,  electric vehicles ,  machine learning ,  random forest ,  gradient Boosting ,  XGBoost

JST classification (1)： Electric vehicles  (QG07020B)

Reference (23)：

• International Energy Agengy (IEA). Global EV Outlook 2018: Towards Corss-Modal Electrification; IEA: Paris, France, 2018; p. 141. International Energy Agengy (IEA). Digitalization & Energy; IEA: Paris, France, 2017; p. 188. European Commission. Directive of the european parliament and of the council on the deployment of alternative fuels infrastructure. Off. J. Eur. Union 2014, 12, 1-38.
• De Gennaro, M.; Paffumi, E.; Martini, G. Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities. Big Data Res. 2016, 6, 11-25.
• [Green Version] Glombeka, M.; Helmus, J.R.; Lees, M.; Hoed van den, R.; Quax, R. Vulnerability of Charging Infrastructure, a Novel Approach for Improving Charging Station Deployment. In Proceedings of the 7th Transport Research Arena TRA 2018, Vienna, Austria, 16-19 April 2018; p. 10.
• Helmus, J.; Van Den Hoed, R. Key Performance Indicators of Charging infrastructure. World Electr. Veh. J. 2016, 8, 733.
• Lucas, A.; Prettico, G.; Flammini, M.; Kotsakis, E.; Fulli, G.; Masera, M. Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis. Energies 2018, 11, 18.

Terms in the title (6)： 教師付き機械学習 ,  充電 ,  インフラストラクチャー ,  EV ,  アイドル時間 ,  推定