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Predicting Electricity Consumption in the Kingdom of Saudi Arabia

Author

Listed:
  • Marwa Salah EIDin Fahmy

    (Sadat Academy for Management Sciences, Cairo 2222, Egypt)

  • Farhan Ahmed

    (Department of Economics & Management Sciences, NED University of Engineering & Technology, Karachi 75270, Pakistan)

  • Farah Durani

    (College of Business Administration, University of Business and Technology, Jeddah 21361, Saudi Arabia)

  • Štefan Bojnec

    (Faculty of Management, University of Primorska, SI-6000 Koper-Capodistria, Slovenia)

  • Mona Mohamed Ghareeb

    (Faculty of High Asian Studies, Zagazig University, Zagazig 31527, Egypt)

Abstract

Forecasting energy consumption in Saudi Arabia for the period from 2020 until 2030 is investigated using a two-part composite model. The first part is the frontier, and the second part is the autoregressive integrated moving average (ARIMA) model that helps avoid the large disparity in predictions in previous studies, which is what this research seeks to achieve. The sample of the study has a size of 30 observations, which are the actual consumption values in the period from 1990 to 2019. The philosophy of this installation is to reuse the residuals to extract the remaining values. Therefore, it becomes white noise and the extracted values are added to increase prediction accuracy. The residuals were calculated and the ARIMA (0, 1, 0) model with a constant was developed both of the residual sum of squares and the root means square errors, which were compared in both cases. The results demonstrate that prediction accuracy using complex models is better than prediction accuracy using single polynomial models or randomly singular models by an increase in the accuracy of the estimated consumption and an improvement of 18.5% as a result of the synthesizing process, which estimates the value of electricity consumption in 2030 to be 575 TWh, compared to the results of previous studies, which were 365, 442, and 633 TWh.

Suggested Citation

  • Marwa Salah EIDin Fahmy & Farhan Ahmed & Farah Durani & Štefan Bojnec & Mona Mohamed Ghareeb, 2023. "Predicting Electricity Consumption in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(1), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:506-:d:1022934
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    References listed on IDEAS

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    Cited by:

    1. Amjad Ali, 2023. "Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    2. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    3. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management," Energies, MDPI, vol. 16(4), pages 1-28, February.

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