Special Issue (SI): Artificial Intelligence and Intelligent Data-Driven Approaches for Sustainable Engineering and Energy Systems

Short Abstract

This Special Issue aims to highlight recent advances in artificial intelligence, machine learning, and data-driven optimization techniques for sustainable engineering and intelligent energy systems. It will focus on innovative computational methods applied to renewable energy forecasting, environmental monitoring, smart healthcare, fault diagnosis, predictive maintenance, and explainable artificial intelligence (XAI). As the demand for reliable, efficient, and sustainable technologies grows, intelligent hybrid models that integrate deep learning, optimization algorithms, and interpretable AI have become essential in both academic research and industrial practice.

We invite high-quality original research articles and comprehensive review papers addressing emerging AI-based methodologies for complex engineering and environmental challenges. Topics of particular interest include renewable energy systems, photovoltaic and wind energy forecasting, smart grids, biomedical engineering applications, predictive analytics, fault detection, optimization-driven decision support systems, and trustworthy AI frameworks.

The Special Issue will provide an interdisciplinary platform for researchers to share state-of-the-art developments and outline future directions in intelligent sustainable systems. All submissions will undergo the journal’s rigorous double-blind peer review process.

 

Keywords

Artificial Intelligence, Machine Learning, Deep Learning, Explainable AI, Renewable Energy Systems, Smart Grids, Predictive Analytics, Sustainable Engineering, Optimization Algorithms

 

Research Background and Motivation

The global transition toward sustainable energy and intelligent engineering systems has created an urgent need for advanced computational methods that can handle complexity, uncertainty, and large-scale data. Artificial intelligence, machine learning, and data-driven optimization have emerged as powerful tools for addressing critical challenges such as renewable energy forecasting, smart grid management, predictive maintenance, and environmental monitoring. However, the deployment of these models in real-world applications still faces significant barriers, including a lack of interpretability, limited robustness, and insufficient integration of domain knowledge. Explainable artificial intelligence (XAI) and hybrid deep learning–optimization frameworks offer promising pathways to overcome these limitations by making AI-driven decisions more transparent, reliable, and aligned with engineering constraints. This Special Issue is motivated by the growing need to bridge the gap between theoretical advances and practical implementation in sustainable engineering. It aims to gather cutting-edge research that advances intelligent, trustworthy, and application-oriented AI solutions for modern energy and engineering systems.

Against this rapidly evolving landscape, this Special Issue seeks to encourage cutting-edge contributions that explore emerging trends, novel methodologies, and practical applications in robotics and automation. Researchers are invited to share innovative theoretical, computational, experimental, and industrial studies that advance intelligent robotic technologies and inspire future developments in automated engineering systems.

  Necessity and Significance of the Special Issue

The rapid expansion of renewable energy systems, smart grids, and intelligent engineering applications has generated complex challenges that traditional analytical methods struggle to address. While artificial intelligence and machine learning offer powerful solutions, their practical deployment is often hindered by the "black-box" nature of many models, limited interpretability, and insufficient robustness for safety-critical systems. There is a pressing need to develop explainable, reliable, and optimization-driven AI frameworks that can be confidently applied in real-world sustainable engineering contexts. This Special Issue is necessary to bring together interdisciplinary research that specifically targets these gaps, integrating deep learning, optimization algorithms, and XAI for energy and environmental applications. Its significance lies in creating a dedicated platform for advancing trustworthy AI methods that bridge theoretical innovation and industrial practice. By gathering cutting-edge contributions, the issue will help shape future research directions and support the global transition toward intelligent, sustainable, and resilient engineering systems.

 

Forward-Thinking Insights and Research Inspiration

As intelligent systems become increasingly embedded in sustainable engineering and energy infrastructures, the next wave of innovation will be defined by trustworthy, autonomous, and human-centric AI. Forward-thinking research is moving beyond purely predictive accuracy toward models that are inherently explainable, robust under distribution shifts, and capable of incorporating physical laws and domain constraints. Emerging paradigms such as physics-informed machine learning, federated learning for privacy-preserving smart grids, and digital twins for predictive maintenance are poised to reshape the landscape. At the same time, the integration of large language models and foundation models with domain-specific engineering knowledge opens new frontiers for automated design, diagnosis, and decision-making. The path forward also demands solutions to pressing ethical and societal challenges, including fairness, accountability, and energy-efficient AI, ensuring that technological progress aligns with sustainability goals. This Special Issue aspires to capture these forward-looking trends and inspire researchers to propose novel methodologies that bridge the gap between cutting-edge AI and real-world sustainable systems. We encourage contributions that not only address current limitations but also envision the future of intelligent engineering—where AI acts as a transparent, reliable partner in achieving global sustainability. By offering a dedicated platform, we hope to spark interdisciplinary collaborations and set the agenda for the next generation of research in this dynamic and impactful field.

 

Proposed Research Topics (include, but are not limited to)

1. Deep learning and hybrid models for renewable energy forecasting

2. Machine learning for photovoltaic and wind power prediction

3. Explainable artificial intelligence (XAI) in energy and engineering applications

4. Smart grid optimization and intelligent energy management

5. Predictive maintenance and fault diagnosis using AI techniques

6. AI-driven biomedical engineering and smart healthcare systems

7. Environmental monitoring and data-driven sustainability assessment

8. Optimization algorithms for sustainable engineering design

9. Physics-informed machine learning for energy systems

10. Digital twins and simulation-driven decision support systems

11. Trustworthy AI: fairness, robustness, and interpretability

12. Edge computing and lightweight AI for real-time monitoring

13. Federated learning for privacy-preserving smart systems

14. Data-driven approaches for climate resilience and adaptation

15. Integration of domain knowledge with data-driven models

 

Guest Editors

Dr. Yıldırım Özüpak

Affiliation: Dicle University, Türkiye

Scopus：https://www.scopus.com/authid/detail.uri?authorId=57200142934

ORCID：https://orcid.org/0000-0001-8461-8702

Email：yildirim.ozupak@dicle.edu.tr

 

Timeline

Submission Opens: 27 May 2026

Submission Deadline: 31 March 2027

 

Editorial Process Timeline

Preliminary Review: 1 week

Peer Review: 4-6 weeks

Final Decision: 2 weeks

Authors are encouraged to submit their manuscripts through the journal's online submission system, adhering to the submission guidelines provided on the journal's website.

 

Submit Options

- OJS: https://ojs.wiserpub.com/index.php/EST/about/submissions

- Email: est@universalwiser.com

 

 Contact for Inquiries

Dr. Yıldırım Özüpak
Affiliation: Dicle University, Türkiye

Email：yildirim.ozupak@dicle.edu.tr

 

We look forward to your contributions to this endeavor!

 

Michelle Zu

Journal Coordinator

Email: est@universalwiser.com