Advanced Machine Learning and Data Analysis Technologies in Modern Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 27 January 2026 | Viewed by 19
Special Issue Editor
Interests: fault diagnosis; fault detection; fault prediction; fault visualization; fault-tolerant control; self-healing; performance evaluation; data-driven modeling; mechanism modeling; interpretable machine learning; process knowledge integration; signal processing; industrial big data; process industry; electromechanical systems
Special Issue Information
Dear Colleagues,
The ongoing global transition toward clean, intelligent, and resilient energy infrastructures is driving a profound transformation in the way modern energy systems are modeled, monitored, and optimized. These systems—spanning from smart grids and renewable generation units to integrated energy hubs and industrial power systems—are increasingly characterized by their scale, heterogeneity, dynamism, and real-time data richness.
In this context, advanced machine learning and data analysis technologies have emerged as indispensable tools in addressing the operational challenges of modern energy systems. From improving the accuracy of load forecasting and renewable energy prediction, to enabling early fault detection, robust optimization, and self-healing control strategies, these techniques are reshaping the landscape of energy system intelligence.
This Special Issue aims to present and promote cutting-edge research that explores how data-driven methods, intelligent algorithms, and explainable artificial intelligence (XAI) can contribute to the modeling, analysis, and control of modern energy systems. We particularly encourage submissions that bridge the gap between theoretical innovation and practical application in energy domains, ensuring both technical robustness and operational interpretability.
Topics of interest include, but are not limited to, the following:
- Machine learning for load forecasting, renewable generation, and energy demand modeling;
- Fault diagnosis, prognosis, and resilient control in power generation and distribution systems;
- Data-driven modeling of complex energy processes and hybrid energy systems;
- Explainable AI and interpretable machine learning in energy system monitoring;
- Residual signal analysis and zero-dynamics methods for energy system fault detection;
- Multi-source heterogeneous data fusion for smart energy management;
- Digital twins and virtual sensors in energy system operation and optimization;
- Real-time anomaly detection and predictive maintenance in energy infrastructures;
- Optimization of integrated energy systems using reinforcement learning and metaheuristics;
- Applications in power grids, microgrids, smart buildings, and industrial energy systems.
We welcome original research articles, comprehensive reviews, and application-oriented studies that demonstrate the effectiveness of intelligent data analysis in advancing the reliability, efficiency, and transparency of energy systems.
Dr. Yongjian Wang
Guest Editor
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. Energies 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 2600 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
- machine learning in energy systems
- fault diagnosis and resilient control
- data-driven energy optimization
- multi-source data fusion
- explainable artificial intelligence (XAI)
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