Naslov
Using neural network modelling for estimation and forecasting of transport sector energy demand in developing countries

Autori
Maaouane, Mohamed ; Chennaif, Mohammed ; Zouggar, Smail ; Krajačić, Goran ; Duić, Neven ; Zahboune, Hassan ; Kerkour ElMiad, Aissa

Izvornik
Energy conversion and management (0196-8904) 258 (2022); 115556, 0

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
ANN ; Transport sector ; Energy efficiency indicators ; Energy demand ; Bottom-up approach

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

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