Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles

Jarosław Ziółkowski, Mateusz Oszczypała, Jerzy Małachowski and Joanna Szkutnik-Rogoż
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Jarosław Ziółkowski: Faculty of Mechanical Engineering, Institute of Mechanics and Computational Engineering, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
Mateusz Oszczypała: Faculty of Mechanical Engineering, Institute of Mechanics and Computational Engineering, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
Jerzy Małachowski: Faculty of Mechanical Engineering, Institute of Mechanics and Computational Engineering, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
Joanna Szkutnik-Rogoż: Faculty of Mechanical Engineering, Institute of Mechanics and Computational Engineering, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

Energies, 2021, vol. 14, issue 9, 1-23

Abstract: This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order to create a predictive model on the basis of which fuel consumption of motor vehicles can be determined. A database containing 1750 records, being a set of information on vehicles manufactured in last decade, was used in the process of training the artificial neural networks. The MLP (Multi-Layer Perceptron) 22-10-3 network has been selected from the created neural networks, which was further subjected to an analysis. In order to determine if the predicted values match the real values, the linear Pearson correlation coefficient r and coefficient of determination R 2 were used. For the MLP 22-10-3 neural network, the calculated coefficient r was within range 0.93–0.95, while the coefficient of determination R 2 assumed a satisfactory value of more than 0.98. Furthermore, a sensitivity analysis of the predictive model was performed, determining the influence of each input variable on prediction accuracy. Then, a neural network with a reduced number of neurons in the input layer (MLP-20-10-3) was built, retaining a quantity of the hidden and output neurons and the activation functions of the individual layers. The MLP 20-10-3 neural network uses similar values of the r and R 2 coefficients as the MLP 22-10-3 neural network. For the evaluation of both neural networks, the measures of the ex post prediction errors were used. Depending on the predicted variable, the MAPE errors for the validation sets reached satisfactory values in the range of 5–8% for MLP 22-10-3 and 6–10% for MLP 20-10-3 neural network, respectively. The prediction tool described is intended for the design of passenger cars equipped with internal combustion engines.

Keywords: artificial neural networks; prediction; fuel consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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