Smart Analytics for Future Energy Systems
A special issue of Computation (ISSN 2079-3197).
Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 1603
Special Issue Editors
Interests: energy system modeling; renewable energy integration; sustainable energy solutions; energy systems analysis; industrial energy optimization
Interests: energy efficiency; heat exchanger modeling and optimization; local energy and entropy analysis; exergy analysis; thermodynamic optimization
Interests: sustainable energy systems; turbomachinery; energy transition; power plant optimization; organic rankine cycle (ORC); industrial energy systems integration
Special Issue Information
Dear Colleagues,
Renewable energy technologies play a vital role in the transition toward a low-carbon society. This transformation goes beyond merely replacing traditional energy sources to reduce greenhouse gas emissions—it also requires the modernization and optimization of emerging technologies. Advances in Artificial Intelligence (AI), machine learning, and big data analytics offer powerful tools to drive innovation and efficiency in renewable energy systems, resulting in new possibilities for their intelligent management and integration.
This Special Issue invites original research focused on the application of data science in the renewable energy sector, including (but not limited to) the following:
- Optimizing energy production;
- Smart grids and intelligent energy distribution;
- Forecasting energy production and consumption;
- Optimizing energy storage systems (e.g., battery management);
- Managing demand response;
- Fault detection and predictive maintenance;
- Integration of distributed energy resources;
- Real-time energy monitoring and control;
- Energy market forecasting and trading algorithms;
- Behavioral analytics for energy efficiency;
- Geospatial data analysis for the site selection of renewable facilities;
- Optimization of hybrid energy systems;
- Sustainability assessment using data-driven methods;
- Policy impact modeling using big data;
- Climate data modeling for renewable planning;
- Edge and IoT analytics for decentralized energy management.
We also welcome other relevant contributions that explore how advanced analysis methods can support and accelerate the clean energy transition. By using advanced methods, the renewable energy sector can develop more adaptive and resilient strategies to meet the challenges posed by climate change. We invite contributions that provide novel insights, methodological innovations, and practical solutions to support the energy transition.
Dr. Marina Budanko
Dr. Martina Odeljan
Dr. Zvonimir Guzović
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 250 words) can be sent to the Editorial Office for assessment.
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. Computation 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 1800 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
- data science
- energy transition
- machine learning
- energy efficiency
- big data analytics
- renewable energy
- sustainability
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