Prediction of Transportation Energy Demand by Novel Hybrid Meta-Heuristic ANN

2021 ◽  
Author(s):  
Mohammad Ali Sahraei ◽  
Merve Kayaci Çodur
Keyword(s):  
Energy Demand ◽  
Transportation Energy

Prediction of transportation energy demand: Multivariate Adaptive Regression Splines

Energy ◽  
2021 ◽  
Vol 224 ◽  
pp. 120090
Author(s):  
Mohammad Ali Sahraei ◽  
Hakan Duman ◽  
Muhammed Yasin Çodur ◽  
Ecevit Eyduran
Keyword(s):  
Energy Demand ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Transportation Energy ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Transportation energy demand in Asia

2017 ◽  
pp. 81-101
Author(s):  
Govinda R. Timilsina ◽  
Ashish Shrestha
Keyword(s):  
Energy Demand ◽  
Transportation Energy

Transportation energy demand

1974 ◽  
pp. 293-314
Author(s):  
Donald N. Dewees
Keyword(s):  
Energy Demand ◽  
Transportation Energy

Transport Energy Demand Modeling of the United States Using Artificial Neural Networks and Multiple Linear Regressions

2014 ◽  
Author(s):  
Arash Kialashaki ◽  
John Reisel
Keyword(s):  
Neural Network ◽  
United States ◽  
Energy Demand ◽  
Regression Models ◽  
The United States ◽  
Transportation Energy ◽  
Independent Variables ◽  
Transportation Sector ◽  
The Future ◽  
Artificial Neural

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends. This paper describes the development of two types of transportation energy models which are able to predict the United States’ future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested. The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030. In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends. Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

scholarly journals Forecast of transportation energy demand through the year 2010

1991 ◽  
Author(s):  
M.M. Mintz ◽  
A.D. Vyas
Keyword(s):  
Energy Demand ◽  
Transportation Energy

Study on the Passenger Transportation Energy Demand and Carbon Emission of Jilin Province Based on LEAP Model

2012 ◽  
Vol 518-523 ◽  
pp. 2243-2246 ◽  
Author(s):  
Zhuo Ma ◽  
Yong Xuan Wang ◽  
Hai Yan Duan ◽  
Xian En Wang ◽  
De Ming Dong
Keyword(s):  
Energy Demand ◽  
Carbon Emission ◽  
Jilin Province ◽  
Baseline Scenario ◽  
Low Carbon ◽  
Transportation Industry ◽  
Transportation Energy ◽  
Passenger Transportation ◽  
Term Energy

With the continuous development of the economic economy, the demands for automobiles in Jilin province increase constantly. The carbon emission control of transportation department will become one of the key fields for greenhouse gas control in Jilin province. This paper employs the LEAP Model, through setting Baseline scenario and Low-carbon scenario, to imitate the long-term energy demand and carbon emission of urban passenger transport in Jilin province. Then after the comparative analysis, this paper investigates the major impact elements and feasible paths for Jilin’s transportation industry carbon emission.

scholarly journals Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing

2018 ◽  
Vol 10 (6) ◽  
pp. 2033 ◽  
Author(s):  
Xianchun Tan ◽  
Yuan Zeng ◽  
Baihe Gu ◽  
Yi Wang ◽  
Baoguang Xu
Keyword(s):  
Scenario Analysis ◽  
Energy Demand ◽  
Ghg Emissions ◽  
Road Transportation ◽  
Transportation Energy ◽  
Urban Road

Possibilities of Trolleybus Transportation Energy Demand Reduction

2015 ◽  
Vol 70 (1) ◽  
pp. 301-304
Author(s):  
P. Vorel ◽  
P. Prochazka ◽  
I. Pazdera ◽  
D. Cervinka ◽  
J. Martis ◽  
...  
Keyword(s):  
Energy Demand ◽  
Transportation Energy ◽  
Demand Reduction
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