Smart Hydropower Water Distribution Networks, Use of Artificial Intelligence Methods and Metaheuristic Algorithms to Generate Energy from Existing Water Supply Networks

Karakatsanis, Diamantis; Theodossiou, Nicolaos

doi:10.3390/en15145166

Cited by 5 publications

(1 citation statement)

References 68 publications

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“…The current research trend on metaheuristic methods, highlighted in the most recent publications, such as Karakatsanis and Theodossiou (2022) and Samadi-Koucheksaraee et al ( 2022), suggests a focus on hybridization, integrating multiple metaheuristic methods to leverage their strengths and overcome their weaknesses. Hybrid algorithms combine the exploration capabilities of Genetic Algorithms with the exploitation abilities of other metaheuristic methods, or combine Machine Learning with different metaheuristic algorithms (Sangroula et al 2022).…”

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

Review of Metaheuristic Methodologies for Leakage Reduction and Energy Saving in Water Distribution Networks

Ferrarese,

Medoukali,

Mirauda

et al. 2024

Water Resour Manage

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Metaheuristic methods have emerged as powerful tools for solving complex optimization problems in various domains, including the sustainability of water distribution systems. They provide efficient and effective solutions by mimicking natural processes and searching for the optimal option within a large solution space. Despite the existence of these methods in the water distribution field for several years, a direct comparison between the various proposed solutions often proves challenging, due to the different parameter definitions used by the authors. The present review presents the solutions proposed by a total of 36 research papers taken from the Web of Science, Scopus and Google Scholar databases focusing on the application of metaheuristic methods for leakage reduction and energy saving in water distribution networks. The review is intended to facilitate comparative analysis among the solutions proposed by authors concerning key aspects of the optimization process. These aspects include the definition of the algorithm, the specification of the objective function, and the strategies employed for reducing the search space. The characteristics of the networks used as case studies by the reviewed papers are also presented to allow the reader to evaluate the applicability of the solutions to specific networks.

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Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

Review of Metaheuristic Methodologies for Leakage Reduction and Energy Saving in Water Distribution Networks

Ferrarese,

Medoukali,

Mirauda

et al. 2024

Water Resour Manage

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Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization

El-Shafeiy

Abohany

Elmessery

et al. 2023

Neural Comput & Applic

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Coconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water.

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Powering the future: Harnessing the potential of machine learning algorithms over classical optimization methods for run-of-river hydropower plants

Aminaddinov,

Bahrami

2024

AIP Conference Proceedings

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Smart Hydropower Water Distribution Networks, Use of Artificial Intelligence Methods and Metaheuristic Algorithms to Generate Energy from Existing Water Supply Networks

Cited by 5 publications

References 68 publications

Review of Metaheuristic Methodologies for Leakage Reduction and Energy Saving in Water Distribution Networks

Review of Metaheuristic Methodologies for Leakage Reduction and Energy Saving in Water Distribution Networks

Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization

Powering the future: Harnessing the potential of machine learning algorithms over classical optimization methods for run-of-river hydropower plants

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