Artificial intelligence for water-energy nexus demand forecasting: A review

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

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

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.

Original languageBritish English
Pages (from-to)730-744
Number of pages15
JournalInternational Journal of Low-Carbon Technologies
Volume17
DOIs
StatePublished - 2022

Keywords

  • artificial intelligence
  • energy demand
  • forecasting
  • water demand

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