Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.

Download Full-text

Application of ANFIS and LLNF models to automobile fuel consumption prediction: A comparative study

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) ◽

10.1109/isspa.2012.6310650 ◽

2012 ◽

Cited By ~ 1

Author(s):

Mohammad Reza Rafimanzelat ◽

Seyed Hossein Iranmanesh

Keyword(s):

Comparative Study ◽

Fuel Consumption ◽

Consumption Prediction

Download Full-text

Forecasting the Fuel Consumption of Passenger Ships with a Combination of Shallow and Deep Learning

Electronics ◽

10.3390/electronics9050776 ◽

2020 ◽

Vol 9 (5) ◽

pp. 776 ◽

Cited By ~ 1

Author(s):

Ioannis Panapakidis ◽

Vasiliki-Marianna Sourtzi ◽

Athanasios Dagoumas

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Fuel Consumption ◽

Initial Study ◽

Forecasting Model ◽

Time Series Models ◽

Prediction System ◽

Fuel Management ◽

Passenger Ships ◽

Consumption Prediction

An accurate fuel consumption prediction system for transportation units is the pillar that a more efficient fuel management can rely on. This in turn may eventually lead to cost and emission savings for the unit’s owner. Numerous studies have been conducted for predicting the fuel usage in various means of transportation (i.e., airplanes, trucks, and vehicles). However, there is a limited number of researches that focus on passenger ships. These researches involve traditional machine learning models. There is a lack of literature on deep-learning-based forecasting models. The present paper serves as an initial study for exploring the potential of deep learning in day-ahead fuel consumption on a passenger ship. Firstly, a discussion is provided for the parameters that influence the fuel consumption. Secondly, the day-ahead fuel forecasting problem is formulated. To fully examine the influence of exogenous parameters on the consumption, various scenarios are formulated that differ in the types and number of inputs. The proposed forecasting model combines shallow and deep learning. Several machine learning and time series models were compared, and the results indicate the robustness of the proposed approach.

Download Full-text