Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimation of Real-world Fuel Consumption Rate of Light-duty Vehicles Based on Big Data

Version 1 : Received: 1 November 2021 / Approved: 2 November 2021 / Online: 2 November 2021 (09:40:05 CET)

A peer-reviewed article of this Preprint also exists.

Zeng, I.Y.; Tan, S.; Xiong, J.; Ding, X.; Li, Y.; Wu, T. Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners. Energies 2021, 14, 7915. Zeng, I.Y.; Tan, S.; Xiong, J.; Ding, X.; Li, Y.; Wu, T. Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners. Energies 2021, 14, 7915.

Journal reference: Energies 2021, 14, 7915
DOI: 10.3390/en14237915

Abstract

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Keywords

Real-world fuel consumption rate; machine learning; big data; light-duty vehicle; China

Subject

SOCIAL SCIENCES, Microeconomics and Decision Sciences

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