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

Abstract

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.