Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM
Author
Abstract
Suggested Citation
DOI: 10.1016/j.energy.2024.131221
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
- Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
- Ning Yang & Lei Yang & Feng Xu & Xue Han & Bin Liu & Naiyuan Zheng & Yuan Li & Yu Bai & Liwei Li & Jiguang Wang, 2022. "Vehicle Emission Changes in China under Different Control Measures over Past Two Decades," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
- Mulholland, Eamonn & Teter, Jacob & Cazzola, Pierpaolo & McDonald, Zane & Ó Gallachóir, Brian P., 2018. "The long haul towards decarbonising road freight – A global assessment to 2050," Applied Energy, Elsevier, vol. 216(C), pages 678-693.
- Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
- Jian Gong & Junzhu Shang & Lei Li & Changjian Zhang & Jie He & Jinhang Ma, 2021. "A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors," Energies, MDPI, vol. 14(23), pages 1-18, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
- Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
- Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
- Santos, Alberto & Maia, Pedro & Jacob, Rodrigo & Wei, Huang & Callegari, Camila & Oliveira Fiorini, Ana Carolina & Schaeffer, Roberto & Szklo, Alexandre, 2024. "Road conditions and driving patterns on fuel usage: Lessons from an emerging economy," Energy, Elsevier, vol. 295(C).
- Mehdi Jahangir Samet & Heikki Liimatainen & Oscar Patrick René van Vliet & Markus Pöllänen, 2021. "Road Freight Transport Electrification Potential by Using Battery Electric Trucks in Finland and Switzerland," Energies, MDPI, vol. 14(4), pages 1-22, February.
- Xiaxuan He & Qifeng Yuan & Yinghong Qin & Junwen Lu & Gang Li, 2024. "Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones," Land, MDPI, vol. 13(10), pages 1-34, October.
- Sascha O. Becker & Hans-Joachim Voth, 2023.
"From the Death of God to the Rise of Hitler,"
CESifo Working Paper Series
10730, CESifo.
- Becker, Sascha O. & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," CAGE Online Working Paper Series 688, Competitive Advantage in the Global Economy (CAGE).
- Becker, Sascha O. & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," CEPR Discussion Papers 18543, C.E.P.R. Discussion Papers.
- Sascha O. Becker & Hans-Joachim Voth, 2023. "From the Death of God to the Rise of Hitler," CEH Discussion Papers 03, Centre for Economic History, Research School of Economics, Australian National University.
- Becker, Sascha O. & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," IZA Discussion Papers 16538, Institute of Labor Economics (IZA).
- Sakiru Adebola Solarin & Muhammed Sehid Gorus & Onder Ozgur, 2024. "Modelling the economic effect of inbound birth tourism: a random forest algorithm approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4223-4240, October.
- Murat Aslan & Onder Ozgur, 2024. "Financial dollarization and its effects on inflation and output in Turkey: a machine learning approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5777-5804, December.
- Maria A. F. Silva Dias & Yania Molina Souto & Bruno Biazeto & Enzo Todesco & Jose A. Zuñiga Mora & Dylana Vargas Navarro & Melvin Pérez Chinchilla & Carlos Madrigal Araya & Dayanna Arce Fernández & Be, 2024. "Reduction of Wind Speed Forecast Error in Costa Rica Tejona Wind Farm with Artificial Intelligence," Energies, MDPI, vol. 17(22), pages 1-12, November.
- Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
- Lamperti, Fabio, 2024. "Unlocking machine learning for social sciences: The case for identifying Industry 4.0 adoption across business restructuring events," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
- Matteo Prussi & Lorenzo Laveneziana & Lorenzo Testa & David Chiaramonti, 2022. "Comparing e-Fuels and Electrification for Decarbonization of Heavy-Duty Transports," Energies, MDPI, vol. 15(21), pages 1-17, October.
- Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024.
"ddml: Double/debiased machine learning in Stata,"
Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
- Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann & Achim Ahrens, 2022. "ddml: Double/debiased machine learning in Stata," Swiss Stata Conference 2022 02, Stata Users Group.
- Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2023. "ddml: Double/Debiased Machine Learning in Stata," IZA Discussion Papers 15963, Institute of Labor Economics (IZA).
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2023. "ddml: Double/debiased machine learning in Stata," Papers 2301.09397, arXiv.org, revised Jan 2024.
- Landry Frank Ineza Havugimana & Bolan Liu & Fanshuo Liu & Junwei Zhang & Ben Li & Peng Wan, 2023. "Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis," Energies, MDPI, vol. 16(3), pages 1-25, January.
- Hillebrecht, Michael & Klonner, Stefan & Pacere, Noraogo A., 2020. "Dynamic Properties of Poverty Targeting," Working Papers 0696, University of Heidelberg, Department of Economics.
- Shankar, Ravi & Pathak, Devendra Kumar & Choudhary, Devendra, 2019. "Decarbonizing freight transportation: An integrated EFA-TISM approach to model enablers of dedicated freight corridors," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 85-100.
- Ivan Brandić & Alan Antonović & Lato Pezo & Božidar Matin & Tajana Krička & Vanja Jurišić & Karlo Špelić & Mislav Kontek & Juraj Kukuruzović & Mateja Grubor & Ana Matin, 2023. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models," Energies, MDPI, vol. 16(2), pages 1-10, January.
- David Simon & Aaron Sojourner & Jon Pedersen & Heidi Ombisa Skallet, 2024.
"Financial Incentives for Adoption and Kin Guardianship Improve Achievement for Foster Children,"
Upjohn Working Papers
24-401, W.E. Upjohn Institute for Employment Research.
- Simon, David & Sojourner, Aaron & Pedersen, Jon & Ombisa Skallet, Heidi, 2024. "Financial Incentives for Adoption and Kin Guardianship Improve Achievement for Foster Children," IZA Discussion Papers 17057, Institute of Labor Economics (IZA).
- David Simon & Aaron Sojourner & Jon Pedersen & Heidi Ombisa Skallet, 2024. "Financial Incentives for Adoption and Kin Guardianship Improve Achievement for Foster Children," NBER Working Papers 32560, National Bureau of Economic Research, Inc.
More about this item
Keywords
Heavy duty trucks; Fuel consumption prediction; Machine learning; Model interpretation;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009940. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.