Application of Intelligent Computational Techniques in Power Plants: A review

Firas B. Ismail *, Ammar Al-Bazi, Rami Al-Hadeethi, Deshvin Singh

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool.
Original languageEnglish
Pages (from-to)10-21
Number of pages12
JournalAdvances In Industrial Engineering And Management
Issue number1
Publication statusPublished - 6 Aug 2021

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.


  • intelligent computational tools
  • power plant
  • energy efficiency
  • artificial neural networks
  • genetic algorithms
  • hybrid intelligent systems


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