Machine learning approaches for predicting household transportation energy use-Reference-Cited by-同舟云学术

Machine learning approaches for predicting household transportation energy use

Published:2020-08 Issue: Volume:7 Page:100044
ISSN:2590-2520
Container-title:City and Environment Interactions
language:en
Short-container-title:City and Environment Interactions

Author:

Shams Amiri Shideh,Mostafavi Nariman,Lee Earl Rusty,Hoque Simi

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Urban Studies,Environmental Science (miscellaneous),Geography, Planning and Development

Reference52 articles.

1. World urbanization prospects: The 2014 revision;UN;United Nations Department of Economics and Social Affairs, Population Division: New York, NY, USA,2015

2. Global Transportation Energy Consumption: Examination of Scenarios to 2040 using ITEDD;ITEDD;U.S. Department of Energy,2015

3. Annual Energy Outlook;U.S. Energy Information Administration,2020

4. Residential location, travel, and energy use in the Hangzhou Metropolitan Area;Næss;J Transp Land Use,2010

5. Exploring relationships between building and transportation energy use of residents in US metropolitan regions;Pede,2014

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Relationships between Transportation Expenditures and Built Environment in the United States: Insights from Interpretable Machine-Learning Approach;Journal of Planning Education and Research;2024-08-19

2. Numerical Investigation of the Combustion Characteristics of a Hydrogen-Fueled Engine with Water Injection;Fire;2024-08-19

3. Artificial intelligence and policy making; can small municipalities enable digital transformation?;International Journal of Production Economics;2024-08

4. An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector;Energy;2024-01

5. A review on the applicability of machine learning techniques to the metamodeling of energy systems;Numerical Heat Transfer, Part B: Fundamentals;2023-11-30