Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

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dc.authoridCAVUS, MUHAMMED/0000-0002-6231-6129en_US
dc.authoridAKBULUT, OSMAN/0000-0002-3949-2845en_US
dc.authoridCengiz, Mehmet/0000-0003-4972-167Xen_US
dc.authoridForshaw, Matthew/0000-0001-7014-9837en_US
dc.authoridAllahham, Adib/0000-0002-6123-1086en_US
dc.authorscopusid58614335700en_US
dc.authorscopusid58045001800en_US
dc.authorscopusid57225730695en_US
dc.authorscopusid23007547900en_US
dc.authorscopusid23970832700en_US
dc.authorscopusid55606562100en_US
dc.authorwosidCAVUS, MUHAMMED/HLX-8537-2023en_US
dc.contributor.authorAkbulut, Osman
dc.contributor.authorCavus, Muhammed
dc.contributor.authorCengiz, Mehmet
dc.contributor.authorAllahham, Adib
dc.contributor.authorGiaouris, Damian
dc.contributor.authorForshaw, Matthew
dc.date.accessioned2024-08-23T16:03:37Z
dc.date.available2024-08-23T16:03:37Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractMicrogrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.en_US
dc.description.sponsorshipNewcastle University; Ministry of National Education, Turkeyen_US
dc.description.sponsorshipWe thank the Ministry of National Education, Turkey for financially supporting Osman Akbulut, Muhammed Cavus, and Mehmet Cengiz's PhD study at Newcastle University, UK.en_US
dc.identifier.doi10.3390/en17102260
dc.identifier.issn1996-1073
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85194234561en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/en17102260
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13823
dc.identifier.volume17en_US
dc.identifier.wosWOS:001232303500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofEnergiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep learningen_US
dc.subjectenergy management systemen_US
dc.subjectoptimizationen_US
dc.subjectmicrogriden_US
dc.subjectresilienceen_US
dc.subjectrule-based controlen_US
dc.subjectstabilityen_US
dc.subjectEnergy Managementen_US
dc.subjectAlgorithmen_US
dc.subjectOperationen_US
dc.titleHybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniquesen_US
dc.typeArticleen_US

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