Application of Artificial Intelligence (AI) in Traditional Energy and New Energy
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 1302
Special Issue Editors
Interests: machine learning; multiscale simulation;carbon capture; energy dissipation; battery energy storage
Interests: machine learning; reaction kinetics; quantum chemistry; molecular design; carbon capture
Interests: intelligent modeling; control and optimization of industrial processes; computer vision and its industrial applications; performance monitoring and evaluation of complex industrial processes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As the world undergoes a new round of technological revolution and industrial transformation, the energy industry is embracing the transition towards low-carbon, smart, and sustainable development. This shift is driving profound systemic changes in the economy and society at large. The transformation in energy field includes two key aspects: the "low-carbonization of traditional energy" and the "practical application of low-carbon new energy."
In this context, machine learning (ML) has emerged as a transformative tool that can address these challenges by optimizing energy production, improving system efficiency, and advancing the development of new materials for energy conversion and storage. The integration of machine learning into energy systems promises to accelerate innovation in areas such as smart grids, energy storage, renewable energy technologies, and the design of energy-efficient materials.
Recently, a growing number of graduate and doctoral students have started to investigate the application of machine learning in their research. While their work may appear straightforward due to several limitations, it is underpinned by unique data pertinent to their specific fields and involves distinct methodologies. Consequently, this Special Issue supports early-career researchers in their understanding of the intersection between machine learning and energy, providing particular support to emerging AI and energy professionals.
This Special Issue aims to explore the latest research on the application of machine learning in energy technologies, with a particular focus on both theoretical and experimental advancements. We invite contributions that delve into the following key areas:
- Data-Driven Energy Material Design: Leveraging machine learning to design and optimize new materials for energy storage and conversion, including batteries, catalysts, and other essential energy materials.
- Machine Learning in Energy System Optimization: Exploring the role of machine learning in enhancing the efficiency and management of energy systems, such as optimizing smart grids, managing renewable energy sources, and improving energy storage solutions.
- Advanced Characterization and Monitoring in Energy Systems: Utilizing AI-driven techniques for real-time monitoring, predictive maintenance, and failure detection in energy systems, including power plants, battery systems, and renewable energy installations.
- Machine Learning for Low-Carbon New Energy Applications: Focusing on how machine learning techniques can accelerate the development and application of low-carbon and renewable energy technologies, such as solar, wind, and hydrogen energy.
- Innovations in Machine Learning Techniques for Energy: Introducing novel machine learning models, such as deep learning, reinforcement learning, and optimization algorithms, and their application in solving complex problems within the energy sector.
- Machine Learning for Low-Carbon Traditional Energy Applications: Focusing on how machine learning techniques can facilitate the development and application of carbon capture, utilization, and storage techniques.
We welcome original research articles, review papers, and short communications that provide valuable insights into how machine learning is reshaping the energy landscape. This Special Issue will serve as a platform for advancing the adoption of AI-driven solutions in the energy sector, facilitating the transition to more sustainable, efficient, and intelligent energy systems.
Dr. Tianhang Zhou
Dr. Qilei Liu
Dr. Xin Peng
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. Processes is an international peer-reviewed open access monthly 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
- machine learning in energy
- low-carbonization of traditional energy
- energy system optimization
- smart grids and energy management
- data-driven material design
- renewable energy technologies
- AI-driven monitoring and maintenance
- sustainable energy systems
- innovative machine learning techniques
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