Use of artificial neural networks for transport energy demand modeling
Use of artificial neural networks for transport energy demand modeling
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DOI：
10.1016/j.enpol.2005.02.010
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发表时间：
2006-11-01
期刊：
影响因子：
9
通讯作者：
Ceylan, Halim
中科院分区：
文献类型：
--
作者：
Murat, Yetis Sazi;Ceylan, Halim
提出了一种基于有监督神经网络的交通能源需求预测方法，利用社会经济指标和交通相关指标预测交通能源需求。建立了交通能源需求的人工神经网络模型。实际预测采用前馈神经网络，并用反向传播算法进行训练。为了考察社会经济指标对交通能源需求的影响，根据1970年至2001年的历史能源数据，基于国民生产总值(GNP)、人口和年平均总里程对交通能源需求进行了人工神经网络分析。将模型预测与测试期内的能源数据进行比较，进行模型验证。这些预测是根据两种情况进行的。结果表明，对于因变量和自变量，人工神经网络都能反映历史数据的波动。计算结果证明了所采用的方法对交通能耗预测问题的适用性。(C)2005爱思唯尔有限公司。保留所有权利。
The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem. (c) 2005 Elsevier Ltd. All rights reserved.