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Holistic Approaches in Artificial Intelligence and Renewable Energy
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Holistic Approaches in Artificial Intelligence and Renewable Energy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 2586

Special Issue Editors

Dr. Ionut Nica

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: economic cybernetics; complex adaptive systems; artificial intelligence; agent-based modeling; sustainable development; circular economy; operational research; applied mathematics; risk management; financial contagion; business analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nora Monica Chirita

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: economic cybernetics; consumer behavior; systems analysis; systems diagnosis; dynamics; sustainable development; circular economy
Special Issues, Collections and Topics in MDPI journals
Dr. Camelia Delcea

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: economic cybernetics; consumer behavior; agent-based modeling; sustainable development; gray systems theory; neutrosophic theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of Artificial Intelligence (AI) is creating remarkable opportunities to transform and optimize renewable energy systems. Holistic approaches that integrate AI with advanced technologies are opening new pathways to enhance energy efficiency and reduce environmental impact, offering innovative and sustainable solutions to the energy challenges of the future. 

This Special Issue, “Holistic Approaches in Artificial Intelligence and Renewable Energy”, aims to bring together research that explores the potential of AI to revolutionize the production, distribution, and consumption of renewable energy. Whether through predictive models, machine learning, or advanced quantitative methods, these comprehensive approaches are essential for the optimal management of evolving energy systems. 

We invite submissions that explore both theoretical foundations and real-world applications, including case studies, empirical analyses, and comprehensive reviews. Contributions that highlight the use of AI for optimizing energy resources and building resilient, sustainable energy systems are particularly encouraged. We encourage interdisciplinary research that connects AI, cybernetics, and energy economics to provide actionable insights for both academia and industry. Contributions that address the integration of AI in renewable energy systems aligned with the Sustainable Development Goals or apply Circular Economy principles to enhance sustainability in energy management are highly welcomed. Additionally, comparative studies and novel approaches that combine AI with energy investment optimization or focus on the financial aspects of renewable energy portfolios are also of great interest. 

Recommended topics include, but are not limited to, the following: 

  • AI-driven optimization of renewable energy systems;
  • Machine Learning applications in energy efficiency;
  • Predictive models for energy consumption and production;
  • Cybernetic approaches to energy systems modeling and control;
  • Integration of AI in smart grid management;
  • Holistic approaches to energy resource allocation;
  • AI-enhanced resilience of energy infrastructures;
  • Data-driven approaches for renewable energy forecasting;
  • Intelligent automation in sustainable energy solutions;
  • Systems thinking in AI applications for energy;
  • AI-driven renewable energy solutions for Sustainable Development Goals (SDGs);
  • Circular Economy principles applied to renewable energy systems;
  • AI-based optimization of energy storage systems;
  • Cyber-physical systems in renewable energy integration;
  • Economic modeling of AI-driven renewable energy markets;
  • Financial optimization in renewable energy investments using AI.

We look forward to receiving your contributions!

Dr. Ionut Nica
Prof. Dr. Nora Monica Chirita
Dr. Camelia Delcea
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • renewable energy
  • cybernetics
  • energy portfolio optimization
  • smart grids
  • circular economy
  • sustainable development goals
  • energy forecasting
  • energy investment optimization

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Published Papers (5 papers)

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Research

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29 pages, 5021 KiB  
Article
Artificial Intelligence Applied to Computational Fluid Dynamics and Its Application in Thermal Energy Storage: A Bibliometric Analysis
by Edgar F. Rojas Cala, Ramón Béjar, Carles Mateu, Emiliano Borri and Luisa F. Cabeza
Appl. Sci. 2025, 15(13), 7199; https://doi.org/10.3390/app15137199 - 26 Jun 2025
Viewed by 114
Abstract
Computational fluid dynamics became an essential tool for analyzing complex fluid behavior, with applications ranging from aerospace engineering to renewable energy systems. Recent advancements in artificial intelligence further enhanced computational fluid dynamics capabilities, improving computational efficiency and predictive accuracy. However, despite its widespread [...] Read more.
Computational fluid dynamics became an essential tool for analyzing complex fluid behavior, with applications ranging from aerospace engineering to renewable energy systems. Recent advancements in artificial intelligence further enhanced computational fluid dynamics capabilities, improving computational efficiency and predictive accuracy. However, despite its widespread adoption, the integration of artificial intelligence in computational fluid dynamics for thermal energy storage remained an underexplored research area. This study presented a bibliometric analysis of the existing literature on artificial intelligence applications in computational fluid dynamics, with a specific focus on thermal energy storage systems. By comparing two research domains—artificial intelligence in computational fluid dynamics and artificial intelligence in computational fluid dynamics applied to thermal energy storage—this paper identified a significant gap in the latter, as reflected in the low number of publications, limited collaboration networks, and weak citation relationships. While artificial intelligence-driven computational fluid dynamics research expanded across multiple disciplines, its application in thermal energy storage is still in its early stages, highlighting the need for further investigations. The results indicated a growing interest in artificial intelligence-enhanced computational fluid dynamics models for thermal energy storage optimization, particularly in areas such as heat transfer, phase change materials, and system efficiency improvements. The results also included an analysis of leading contributors to this field, along with emerging countries’ contributions. A study of the key publication sources with a high impact in this domain was also included. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
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16 pages, 2607 KiB  
Article
Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost
by Lichun Qi, Takahiro Kawaguchi and Seiji Hashimoto
Appl. Sci. 2025, 15(12), 6861; https://doi.org/10.3390/app15126861 - 18 Jun 2025
Viewed by 162
Abstract
Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection [...] Read more.
Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection method combining artificial hummingbird algorithm (AHA) and XGboost has been proposed. According to GB14287.4—2014, an experimental platform for fault arcs was designed and built to collect fault arc signals. By leveraging the global search capability and dynamic adaptive mechanism of AHA, key feature subsets sensitive to arcs are selected from high-dimensional time–frequency domain features. Combining the parallel computing advantages and regularization strategies of XGBoost, a low-complexity, highly interpretable fault classification model is constructed. The hyperparameters of XGBoost are simultaneously optimized by AHA. Experimental results show that the proposed method achieves a fault arc detection accuracy rate of 98.098%, effectively identifying series arc faults. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
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19 pages, 1607 KiB  
Article
Artificial Intelligence and Digital Twins for Sustainable Waste Management: A Bibliometric and Thematic Review
by Paola Campana, Riccardo Censi, Anna Maria Tarola and Roberto Ruggieri
Appl. Sci. 2025, 15(11), 6337; https://doi.org/10.3390/app15116337 - 5 Jun 2025
Viewed by 600
Abstract
Sustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This study adopts an integrated approach to [...] Read more.
Sustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This study adopts an integrated approach to analyze the state of the art and key research trajectories related to the application of these technologies in waste management. Through a bibliometric analysis based on the Scopus database and mapping with VOSviewer (version 1.6.20), three main thematic clusters were identified: (i) predictive and environmental methods, (ii) sustainability and optimization, and (iii) monitoring and environmental impacts. A qualitative analysis of the 20 most-cited articles further revealed six major research areas, including waste forecasting, recycled materials, process digitalization, and intelligent environmental monitoring. The findings indicate a growing convergence among digitalization, automation, and sustainability. The adopted approach enables the mapping of major research directions and emerging interconnections among AI, the circular economy, and predictive management, providing an up-to-date and systemic perspective on the field. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
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34 pages, 17965 KiB  
Article
Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency
by Helena M. Ramos, João S. T. Coelho, Eyup Bekci, Toni X. Adrover, Oscar E. Coronado-Hernández, Modesto Perez-Sanchez, Kemal Koca, Aonghus McNabola and R. Espina-Valdés
Appl. Sci. 2025, 15(9), 5211; https://doi.org/10.3390/app15095211 - 7 May 2025
Viewed by 663
Abstract
This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution [...] Read more.
This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. It details new methodologies, including strategic energy management and a machine learning (ML) tool for predicting energy surplus and deficits. The hybrid energy module solution for the Port of Avilés was further developed to evaluate the performance of new tools such as the Energy Management Tool (EMTv1), HYbrid for Renewable Energy Solutions (HY4RES), and a commercial model (Hybrid Optimization of Multiple Energy Resources—HOMER) in optimizing renewable energy and storage management. Seven scenarios were analyzed, integrating different energy sources and storage solutions. Using EMTv1, Scenario 1 showed high surplus energy, while Scenario 2 demonstrated grid independence with Pump-as-Turbine (PAT) storage. The HY4RES model was used to analyze Scenario 3, which achieved a positive grid balance, exporting more than imported, and Scenario 4 revealed limitations of the PAT system due to the low power installed. Scenario 5 introduced a 15 kWh battery, efficiently storing and discharging energy, reducing grid reliance, and fully covering energy needs. Using HOMER modeling, Scenario 6 required 546 kWh of grid energy but sold 2385 kWh back. Scenario 7 produced 3450 kWh/year, covering demand, resulting in 1834 kWh of surplus energy and a small capacity shortage (1.41 kWh/year). AI-based ML analysis was applied to five scenarios (the ones with access to numerical results), accurately predicting energy balances and optimizing grid interactions. A neural network time series (NNTS) model trained on average year data achieved high accuracy (R2: 0.9253–0.9695). The ANN model proved effective in making rapid energy balance predictions, reducing the need for complex simulations. A second case analyzed an increase of 80% in demand, confirming the model’s reliability, with Scenario 3 having the highest MSE (0.0166 kWh), Scenario 2 the lowest R2 (0.9289), and Scenario 5 the highest R2 (0.9693) during the validation process. This study highlights AI-driven forecasting as a valuable tool for ports to optimize energy management, minimize grid dependency, and enhance their efficiency. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
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Review

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32 pages, 571 KiB  
Review
Digital Twin of the European Electricity Grid: A Review of Regulatory Barriers, Technological Challenges, and Economic Opportunities
by Bo Nørregaard Jørgensen and Zheng Grace Ma
Appl. Sci. 2025, 15(12), 6475; https://doi.org/10.3390/app15126475 - 9 Jun 2025
Viewed by 518
Abstract
The European Union (EU) is advancing a digital twin of its electricity grid as a flagship initiative to accelerate the dual transitions of decarbonization and digitalization. By creating a real-time virtual replica of the EU-27 power network, policymakers and industry stakeholders aim to [...] Read more.
The European Union (EU) is advancing a digital twin of its electricity grid as a flagship initiative to accelerate the dual transitions of decarbonization and digitalization. By creating a real-time virtual replica of the EU-27 power network, policymakers and industry stakeholders aim to enhance grid efficiency, resilience, and renewable energy integration. This review provides a comprehensive analysis of the three critical dimensions shaping the digital twin’s development: (1) regulatory barriers, including fragmented policies, inconsistent data governance frameworks, and the need for harmonized standards and incentives across member states; (2) technological challenges, such as achieving interoperability, integrating real-time data, developing robust cybersecurity measures, and ensuring scalable infrastructure; and (3) economic opportunities, centered on potential cost savings, optimized asset management, new flexibility services, and pathways for innovation and investment. Drawing on European Commission policy documents, regulatory reports, academic studies, and industry projects like the Horizon Europe TwinEU initiative, this review highlights that significant groundwork has been laid to prototype and federate local grid twins into a cohesive continental system. However, achieving the full potential of a pan-European digital twin will require additional regulatory harmonization, more mature data-sharing protocols, and sustained financial commitment. This review concludes with an outlook on the strategic convergence of policy reforms, collaborative R&D, and targeted funding, emphasizing how institutional momentum, federated architectures, and cross-sector integration are advancing a secure, resilient, and economically viable digital twin that is envisioned as a foundational layer in the operational and planning infrastructure of Europe’s future electricity system. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
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