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Modeling, analysis and forecasting of the Jordan’s transportation sector energy consumption using artificial neural networks

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Abstract

This paper aims to employ Artificial Neural Networks (ANNs) to model, analyze, and forecast energy consumption and needs in the transportation sector of Jordan. The study investigates four key factors: the number of registered vehicles, income level, ownership level, and fuel prices. Data on energy consumption and the independent variables are collected from government and literature sources spanning the years 1985–2020. Various ANNs are carefully examined and optimized to ensure reliable solution convergence. The findings indicate that energy consumption in Jordan’s transportation sector is primarily influenced by the number of vehicles on the road and income levels. Moreover, the ANN-based energy consumption predictions demonstrate higher accuracy when compared to existing literature models. Consequently, the developed ANN model is utilized to forecast the energy requirements of Jordan’s transportation sector for the coming decade. Finally, this paper offers policy and legislation recommendations to assist decision-makers in ensuring a secure future for the country’s transportation sector.

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Data availability

Data used in this research will be made available upon reasonable request.

Abbreviations

ANN:

Neural network

ANFIS:

Adaptive Neuro-Fuzzy inference system

GDP:

Gross domestic product

PSD:

Public security department

DOS:

Department of statistics

MEMR:

Ministry of Energy and Mineral Resources

TOE:

Ton oil equivalent

MAPE:

Mean absolute percentage error

\({E}_{c}\) :

Energy consumption

\({Ec}_{actual,i}\) :

True values of energy consumption

\({Ec}_{predict,i}\) :

Predicted values of energy consumption

V:

Number of registered vehicles

\(OL\) :

Ownership level

\(IL\) :

Income level

\({R}^{2}\) :

Coefficients of determination

\({Y}_{j}\) :

The output variable

\({\theta }_{j}\) :

The bias at the hidden layer

\({w}_{ji}\) :

The connection weight between the input variable and the hidden layer

\({X}_{i}\) :

The input variable

\(f\) :

The transfer function

\({Z}_{{\varvec{t}}+{\varvec{m}}\boldsymbol{ }}\) :

The forecast after \(m\)

\(m\) :

The number of periods ahead to be forecasted

\({a}_{t}\) :

The forecasted intercept

\({b}_{t}\) :

The forecasted slope

\(\alpha\) :

The smoothing constant

\(S_{t}^{\prime } ,\; S_{t}^{\prime \prime }\) :

The single and double exponential smoothing values respectively for time t

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan

    Mohammad A. Gharaibeh & Ayman Alkhatatbeh

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  1. Mohammad A. Gharaibeh
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  2. Ayman Alkhatatbeh
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Correspondence to Mohammad A. Gharaibeh.

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Gharaibeh, M.A., Alkhatatbeh, A. Modeling, analysis and forecasting of the Jordan’s transportation sector energy consumption using artificial neural networks. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04984-w

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