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Analysis and future projections of the electricity demands of the Jordanian household sector using artificial neural networks

Mohammad A. Gharaibeh (Department of Mechanical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan)
Ayman Alkhatatbeh (Department of Mechanical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan)

Journal of Science and Technology Policy Management

ISSN: 2053-4620

Article publication date: 5 February 2024

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Abstract

Purpose

The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector.

Design/methodology/approach

Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R2).

Findings

The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade.

Originality/value

The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

Keywords

Acknowledgements

The authors wish to thank Hashemite University for providing the necessary tools and equipment to perform this research. Also, special thanks go to the Ministry of Energy and Mineral Resources, Central Bank of Jordan, National Electric Power Company and the Department of Statistics for being so helpful in providing the required data elaborated in this paper. Finally, the authors with to show gratitude to Dr Rami Al-Jarrah from the Hashemite University and Dr Ala Hijazi from the German Jordanian University for the invaluable discussions on the processing and implementation of the artificial neural networks presented in this study.

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

Conflict of interest: The authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Citation

Gharaibeh, M.A. and Alkhatatbeh, A. (2024), "Analysis and future projections of the electricity demands of the Jordanian household sector using artificial neural networks", Journal of Science and Technology Policy Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JSTPM-06-2023-0090

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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