Use of artificial neural networks for transport energy demand modeling

Murat, Yetiş Şazi; Ceylan, Halim

doi:10.1016/j.enpol.2005.02.010

Cited by 164 publications

(71 citation statements)

References 16 publications

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“…This is mainly because ANN has very good approximation capabilities and offers additional advantages, such as short development and fast processing times. ANNs are especially useful in predicting problems where mathematical formulae and prior knowledge on the relationship between inputs and outputs are unknown [5,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

et al. 2011

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Section: Introductionmentioning

confidence: 99%

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

et al. 2011

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“…Geem [5] developed ANN models to forecast South Korea's transport energy consumption and demonstrated that ANN produced more robust results. Murat and Ceylan [11] illustrates an ANN approach for the transport energy demand forecasting using socio-economic and transport related indicators.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Rail Transport Petroleum Consumption Using an Integrated Model of Autocorrelation Functions-Artificial Neural Network

Abolfazli¹,

Asadzadeh²,

Salman³

et al. 2014

APH

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This paper presents the application of time-series and artificial neural network for improvement of energy forecasting in rail transport section. An integrated artificial neural network (ANN) model is presented that uses autocorrelation and partial autocorrelation functions to determine the best input variables for ANN. The proposed ANN uses autocorrelation function (ACF) and partial autocorrelation function (PACF

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“…The aim of using GAs in their work was to determine the BPNN's parameters and to improve the accuracy of the estimation. Murat and Ceylan (2006) implemented an artificial neural-network (ANN) process to estimate the cost of energy transportation. Verlinden et al (2008) developed MRA and ANN-based models to estimate the cost of a sheet metal production.…”

Section: Introductionmentioning

confidence: 99%

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

2017

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One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents datadriven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the LevenbergMarquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

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Use of artificial neural networks for transport energy demand modeling

Cited by 164 publications

References 16 publications

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

Forecasting Rail Transport Petroleum Consumption Using an Integrated Model of Autocorrelation Functions-Artificial Neural Network

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

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