Special Issues

AI and Advanced Computational Techniques for Sustainable Renewable Energy Systems

Submission Deadline: 01 March 2026 (closed) View: 888 Submit to Special Issue

Guest Editor(s)

Prof. Mahdi Moghimi

Email: moghimi@iust.ac.ir

Affiliation: Mecheanical engineering, Iran University of science and technology, Narmak, Tehran, 999067, Iran

Homepage:

Research Interests: hydrodynamics, fluid mechanics, experimental modelling, energy modelling

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Dr. Mohammad Javad Raji Asadabadi

Email: raji_m@mecheng.iust.ac.ir

Affiliation: Mecheanical engineering, Iran University of science and technology, Narmak, Tehran, 999067, Iran

Homepage:

Research Interests: CFD, applied thermodynamics, renewable energy, machine learning, hydrogen and fuel cell

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Dr. Farzad Ghafoorian

Email: fghafoor@uccs.edu

Affiliation: Department of Mechanical and Aerospace Engineering, University of Colorado, Colorado Springs, CO 80918, USA

Homepage:

Research Interests: renewable energy, vertical axis wind turbines, fluid mechanics, CFD

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Summary

The global imperative to transition towards sustainable energy systems is driven by the escalating challenges of climate change and ever-increasing energy demands. This special issue zeroes in on the synergistic integration of Artificial Intelligence (AI) with cutting-edge computational techniques—such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), thermodynamic analysis, and various numerical modeling approaches.


The core aim is to revolutionize the design and optimization of renewable energy systems. We're particularly interested in innovative methodologies that expertly combine AI-driven algorithms with physics-based simulations. This powerful combination is expected to significantly boost the performance, energy conversion efficiency, reliability, and thermal management across a spectrum of renewable energy systems, including wind, solar, geothermal, biomass, and energy storage. We invite high-quality contributions, including original research, comprehensive reviews, and insightful case studies. Submissions should highlight how the integration of AI and numerical modeling is shaping the development of the next generation of smart, adaptive, and sustainable energy technologies, with a strong emphasis on improving thermodynamic performance. Both numerical and experimental perspectives are highly encouraged.


Topics of interest for this Special Issue include, but are not limited to:
· AI-driven Design and Optimization of Renewable Energy Components
· Advanced Computational Fluid Dynamics (CFD) for Energy Systems
· Integrated AI and Physics-Based Simulations for System Performance Prediction
· Machine Learning for Renewable Energy Forecasting and Grid Integration
· Thermodynamic and Exergy Analysis Enhanced by AI
· Innovative Energy Storage Applications with AI
· Heat and Mass Transfer in Sustainable Energy Technologies
· Computational Approaches for Hybrid Renewable Energy Systems
· Experimental Validation of AI and Computational Models in Renewable Energy


Keywords

renewable energy systems, sustainable energy, computational fluid dynamics, artificial intelligence, machine learning, energy storage applications, heat and mass transfer, thermodynamics, exergy analysis, energy conversion.

Published Papers


  • Open Access

    ARTICLE

    Data-Driven Voltage Control for Distribution Networks with High Penetration of PVs via Improved DDPG

    Xiaolong Xiao, Linghao Zhang, Ziran Guo, Xiaoxing Lu, Wenqiang Xie, Shukang Lv, Peishuai Li
    Energy Engineering, DOI:10.32604/ee.2026.076751
    (This article belongs to the Special Issue: AI and Advanced Computational Techniques for Sustainable Renewable Energy Systems)
    Abstract The highly increasing integration of distributed photovoltaics (PVs) has brought significant challenges for distribution network voltage control, which struggles with fast and stochastic fluctuations. This paper proposes a data-driven voltage control method utilizing an improved deep deterministic policy gradient (DDPG) to minimize power losses while maintaining voltage within a safe operating range. The data-driven voltage control framework is established first, with the controlling center formulated as the agent. Based on the distribution network system and the voltage control original model, the data-driven voltage control model is formulated via the Markov decision process (MDP). A voltage More >

  • Open Access

    ARTICLE

    An Intelligent Diagnostic Method Based on Automatic Learning of Complex Fault Signatures for Multiple Coupled Faults in Photovoltaic Arrays

    Jianjun He, Hai Zhang, Junzhe Tian, Hongxiang Luo, Yuexin Du, Zhiqing Deng
    Energy Engineering, DOI:10.32604/ee.2026.078867
    (This article belongs to the Special Issue: AI and Advanced Computational Techniques for Sustainable Renewable Energy Systems)
    Abstract To address the limitations of traditional deep learning models in diagnosing multiple coupled faults in photovoltaic arrays—such as manual hyperparameter tuning, susceptibility to local optima, and limited diagnostic accuracy—this study proposes an intelligent diagnostic method. The method integrates an improved sparrow search algorithm (ISSA) with a CNN-Transformer model. First, the original SSA is enhanced by incorporating a Circle chaotic map, a dynamic adaptive weight, and a sine-cosine search strategy to improve its global optimization capability and convergence stability. Second, a CNN-Transformer base model is constructed. This model employs a 1D-CNN to extract local fault features… More >

  • Open Access

    REVIEW

    Artificial Neural Networks and Taguchi Methods for Energy Systems Optimization: A Comprehensive Review

    Mir Majid Etghani, Homayoun Boodaghi
    Energy Engineering, Vol.122, No.11, pp. 4385-4474, 2025, DOI:10.32604/ee.2025.070668
    (This article belongs to the Special Issue: AI and Advanced Computational Techniques for Sustainable Renewable Energy Systems)
    Abstract Energy system optimization has become crucial for enhancing efficiency and environmental sustainability. This comprehensive review examines the synergistic application of Artificial Neural Networks (ANN) and Taguchi methods in optimizing diverse energy systems. While previous reviews have focused on these methods separately, this paper presents the first integrated analysis of both approaches across multiple energy applications. We systematically analyze their implementation in: Internal combustion engines, Thermal energy storage systems, Solar energy systems, Wind and tidal turbines, Heat exchangers, and hybrid energy systems. Our findings reveal that ANN models consistently achieve prediction accuracies exceeding 90% when compared More >

    Graphic Abstract

    Artificial Neural Networks and Taguchi Methods for Energy Systems Optimization: A Comprehensive Review

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