Using neural network modelling for estimation and forecasting of transport sector energy demand in developing countries (CROSBI ID 310200)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Maaouane, Mohamed ; Chennaif, Mohammed ; Zouggar, Smail ; Krajačić, Goran ; Duić, Neven ; Zahboune, Hassan ; Kerkour ElMiad, Aissa
engleski
Using neural network modelling for estimation and forecasting of transport sector energy demand in developing countries
In developing countries, national-level institutions are often limited by key transportation energy efficiency indicators. A transportation model based on 40 artificial neural networks was developed to fill this gap. Data on energy efficiency indicators for 28 European countries have been collected to train a model for predicting these indicators using socio-economic variables. A bottom-up approach is then used to compare the predicted data to the total energy consumption. Morocco is used as a case study because of the absence of its energy efficiency indicators. The model's outstanding performance was proved after calculating energy demand at a highly disaggregated level. The model was used to forecast energy consumption up to 2050, considering a variety of alternative hypotheses. Four long-term energy demand scenarios were evaluated: frozen efficiency, implementation of EU legislation, cars electrification, and modal shift. The redistribution of passenger kilometres and tonne-kilometres as a way of rising average occupancy and average load revealed a significantly greater potential for energy savings. Switching from diesel to biofuel for buses and light cars was also examined as a solution to minimize GHG emissions. The developed model supplies decision-making institutions with the necessary tools for identifying critical issues, implementing policies, and redistributing infrastructure.
ANN ; Transport sector ; Energy efficiency indicators ; Energy demand ; Bottom-up approach
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Podaci o izdanju
258
2022.
115556
0
objavljeno
0196-8904
1879-2227
10.1016/j.enconman.2022.115556