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Artificial intelligence for water–energy nexus demand forecasting: a review
[Modeling and co-optimization of a micro water-energy nexus for smart communities]

Author

Listed:
  • Alya A Alhendi
  • Ameena S Al-Sumaiti
  • Feruz K Elmay
  • James Wescaot
  • Abdollah Kavousi-Fard
  • Ehsan Heydarian-Forushani
  • Hassan Haes Alhelou

Abstract

Demand forecasting is an essential stage in the plan and management of resources for water and electrical utilities. With the emerging of the concept of water–energy nexus and the dependence of both resources on each other, intelligent approaches are needed for such resources’ prediction in smart communities. Over the past few decades, extensive research has been devoted to develop or improve forecasting techniques to accurately estimate the future demand. The purpose of this paper is to review the most important methods in the demand forecasting of both water and energy, focusing mainly on the most recent advancements and future possible trends, hence providing a guide and insight for future research in the field. With the recent developments in artificial intelligence, it has been observed that most research work in this area highlight the artificial intelligence–based models as promising approaches for short-term demand forecasting in terms of performance evaluation or improvement in accuracy. Finally, all metrics used by researchers to assess the water/energy demand forecast are gathered and compared to provide a solid ground for the future works.

Suggested Citation

  • Alya A Alhendi & Ameena S Al-Sumaiti & Feruz K Elmay & James Wescaot & Abdollah Kavousi-Fard & Ehsan Heydarian-Forushani & Hassan Haes Alhelou, 2022. "Artificial intelligence for water–energy nexus demand forecasting: a review [Modeling and co-optimization of a micro water-energy nexus for smart communities]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 520-534.
    • Handle: RePEc:oup:ijlctc:v:17:y:2022:i::p:520-534.
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. Al-Sumaiti, Ameena Saad & Salama, Magdy M.A. & El-Moursi, Mohamed, 2017. "Enabling electricity access in developing countries: A probabilistic weather driven house based approach," Applied Energy, Elsevier, vol. 191(C), pages 531-548.
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