Neural Network Modeling for Fuel Consumption Base on Least Computational Cost Parameters

Illahi, Ana Antoniette C.; Dadios, Elmer P.

doi:10.1109/hnicem48295.2019.9072728

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…All these references show that there is a physical and empirically measured connection between the value of specific factors and the value of the fuel consumption. Thus, it is possible to use them in order to predict the value of the fuel consumption with ML models, as already shown within the literature [13,14,15].…”

Section: Factors For Fuel Consumption In a Vehiclementioning

confidence: 97%

Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

Barbado,

Corcho

2021

Preprint

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Funding informationFuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style.In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between the input factors and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70% of the categories associated to the fuel-factors are similar to the previous literature.With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12% and 15% in a large fleet (more than 1000 vehicles).

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Section: Factors For Fuel Consumption In a Vehiclementioning

confidence: 97%

Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

Barbado,

Corcho

2021

Preprint

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“…Asher [26] and Sun [14] et al applied an artificial neural network to the fuel consumption prediction of hybrid electric vehicles, and the MAE of the model was between 0 and 0.1%. In addition, there are a few studies on the fuel consumption prediction model based on a feedforward neural network (FNN) [80]. Topić et al [6] used vehicle speed to predict the fuel consumption of a bus, and the R 2 of FNN model was more than 0.97.…”

Section: Neural Network Modelmentioning

confidence: 99%

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

Zhao

Hou

et al. 2023

Energies

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Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models’ predictive performance and application status.

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“…Neural Network has good nonlinear mapping ability, adaptive learning ability, and parallel information processing ability (Illahi et al, 2019;Tran, 2019), and thus it has been widely used in prediction (Wysocki et al, 2019;Yuan et al, 2019). Neural Network is the most typical BBM which has good performance in ship energy consumption prediction (Kim et al, 2021;Zheng et al, 2019;Gkerekos and Lazakis, 2020), and it mainly includes BPNN, MLPN, LSTM, DNN, and CNN.…”

Section: Prediction Of Ship Energy Consumption Based On Neural Networkmentioning

confidence: 99%

A comprehensive review on the prediction of ship energy consumption and pollution gas emissions

Wang

Huang

et al. 2022

Ocean Engineering

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Neural Network Modeling for Fuel Consumption Base on Least Computational Cost Parameters

Cited by 11 publications

References 8 publications

Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

A comprehensive review on the prediction of ship energy consumption and pollution gas emissions

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