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A New Planning to Forecasting Fuel Consumption in Iran Transportation Using a Hybrid Algorithm and Artificial Neural Network

Journal of Research in Business, Economics and Management

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Title A New Planning to Forecasting Fuel Consumption in Iran Transportation Using a Hybrid Algorithm and Artificial Neural Network
 
Creator Dehghanbaghi, Maryam
 
Subject —
Artificial Neural Network; Hybrid Algorithm, Imperialist Competitive Algorithm; Forecasting; Simulated Annealing.
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Description Forecasting fuel demand is one of the preconditions for energy planning and management. Fossil fuels, a major part of which is consumed in transportation, are one of the most important energies. Therefore, forecasting fuel demand in transportation is of particular importance. An MLP perceptron neural network has been proposed in this study, which is trained using a Hybrid algorithm. The hybrid algorithm is based on an imperialist competitive algorithm (ICA), in which the specifications of simulated annealing (SA) algorithm have been used. To perform the forecasting, the effective parameters on road transportation including the amounts of ton kilometer of transported goods, person kilometer of passengers, the average age of cargo fleet, and the average age of passenger fleet were identified. The results of the investigation indicate the effective performance of the proposed algorithm in ANN training.
 
Publisher Scitech Research Organisation
 
Contributor —
 
Date 2018-03-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
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Format application/pdf
 
Identifier http://scitecresearch.com/journals/index.php/jrbem/article/view/1426
 
Source Journal of Research in Business, Economics and Management; Vol 10, No 3: JRBEM; 1891-1904
 
Language eng
 
Relation http://scitecresearch.com/journals/index.php/jrbem/article/view/1426/1025
 
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Rights Copyright (c) 2018 Journal of Research in Business, Economics and Management