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Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches

Author

Listed:
  • Abdalrahman Alsulaili

    (Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait)

  • Noor Aboramyah

    (Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait)

  • Nasser Alenezi

    (Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait)

  • Mohamad Alkhalidi

    (Civil Engineering Department, Kuwait University, P.O. Box 5969, Kuwait City 13060, Kuwait)

Abstract

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination ( R 2 ), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R 2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models’ precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models’ effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions.

Suggested Citation

  • Abdalrahman Alsulaili & Noor Aboramyah & Nasser Alenezi & Mohamad Alkhalidi, 2024. "Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches," Sustainability, MDPI, vol. 16(15), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6326-:d:1441745
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    References listed on IDEAS

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    1. Holtedahl, Pernille & Joutz, Frederick L., 2004. "Residential electricity demand in Taiwan," Energy Economics, Elsevier, vol. 26(2), pages 201-224, March.
    2. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    3. Li-Chen Sim, 2023. "Renewable Energy and Governance Resilience in the Gulf," Energies, MDPI, vol. 16(7), pages 1-17, April.
    4. 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.
    5. Moncef Krarti, 2019. "Evaluation of Energy Efficiency Potential for the Building Sector in the Arab Region," Energies, MDPI, vol. 12(22), pages 1-45, November.
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