Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners
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- Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
- Christoffersen, Peter & Jacobs, Kris, 2004.
"The importance of the loss function in option valuation,"
Journal of Financial Economics, Elsevier, vol. 72(2), pages 291-318, May.
- Peter Christoffersen & Kris Jacobs, 2003. "The Importance of the Loss Function in Option Valuation," CIRANO Working Papers 2003s-52, CIRANO.
- Neeraj Dhanraj Bokde & Zaher Mundher Yaseen & Gorm Bruun Andersen, 2020. "ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling," Energies, MDPI, vol. 13(10), pages 1-24, May.
- Peng, Tianduo & Ou, Xunmin & Yuan, Zhiyi & Yan, Xiaoyu & Zhang, Xiliang, 2018. "Development and application of China provincial road transport energy demand and GHG emissions analysis model," Applied Energy, Elsevier, vol. 222(C), pages 313-328.
- Xinyu Liang & Shaojun Zhang & Ye Wu & Jia Xing & Xiaoyi He & K. Max Zhang & Shuxiao Wang & Jiming Hao, 2019. "Air quality and health benefits from fleet electrification in China," Nature Sustainability, Nature, vol. 2(10), pages 962-971, October.
- Shan Jiang & Hsinchun Chen, 2019. "Examining patterns of scientific knowledge diffusion based on knowledge cyber infrastructure: a multi-dimensional network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1599-1617, December.
- Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
- Zhou, Feng & Joshi, Shailesh N. & Rhote-Vaney, Raphael & Dede, Ercan M., 2017. "A review and future application of Rankine Cycle to passenger vehicles for waste heat recovery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1008-1021.
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Cited by:
- Yushan Yang & Nuoya Gong & Keying Xie & Qingfei Liu, 2022. "Predicting Gasoline Vehicle Fuel Consumption in Energy and Environmental Impact Based on Machine Learning and Multidimensional Big Data," Energies, MDPI, vol. 15(5), pages 1-17, February.
- 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.
- Seongin Jo & Hyung Jun Kim & Sang Il Kwon & Jong Tae Lee & Suhan Park, 2023. "Assessment of Energy Consumption Characteristics of Ultra-Heavy-Duty Vehicles under Real Driving Conditions," Energies, MDPI, vol. 16(5), pages 1-18, February.
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Keywords
real-world fuel consumption rate; machine learning; big data; light-duty vehicle; China;All these keywords.
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