Author Identifier (ORCID)
Ferry Jie: https://orcid.org/0000-0002-6287-8471
Abstract
Transportation is one of the necessities of life. Because humans need transportation to move from one location to another. Transportation requires fuel. On the other hand, fuel consumption is important and must be controlled. This is because fuel can come from both renewable and non-renewable energy sources, depending on the type and process of its formation. Several factors influence the fuel efficiency of a car, including the type of engine, vehicle weight, aerodynamics, driving habits, and other vehicle conditions. This research aims to predict car fuel consumption and identify the factors that affect fuel consumption. Several Machine Learning and Statistical Learning methods were used to predict fuel consumption in this research. The best method for predicting fuel consumption is the Bayesian Neural Network because it produces the lowest error value compared to other methods.
Keywords
Bayesian neural networks, energy, fuel consumption, machine learning
Document Type
Conference Proceeding
Date of Publication
1-1-2025
Volume
11
Publication Title
Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES)
Publisher
Kyushu University
School
Markets and Services Research Centre / School of Business and Law
Funders
Bina Nusantara University (085/VRRTT/V/2025)
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
First Page
1239
Last Page
1245
Comments
Permai, S. D., Moniaga, J. V., Lie, Z. S., & Jie, F. (2025). Fuel consumption prediction using Bayesian neural networks. Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES), 11, 1239–1245. https://doi.org/10.5109/7395669