Computational Renewable Energy Solutions
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 437
Special Issue Editor
2. Department of Mathematics, Florida State University, Tallahassee, FL 32306-4510, 208 Love Building, 1017 Academic Way, USA
Interests: computational and data-enabled science and engineering in energy systems; data assimilation; machine learning; Bayesian learning; computational fluid dynamics; optimisation; power system modelling; computational e-mobility problems; microscale and mesoscale atmospheric, ocean, and radiation models; wind farm and solar farm models; disaster resilience for power systems in renewable energy
Special Issue Information
Dear Colleagues,
Realizing the full business potential of alternative and renewable energy will require advances in the underlying technologies, as well as adaptations in the existing energy infrastructure. High-performance computing capabilities and state-of-the-art numerical simulation models will play a key role in accelerating the business progress necessary to fulfil this potential, and is critical to advancing our understanding of the fundamentals of renewable energy business, from the smallest spatial and temporal scales to the integration and design of full systems. Advances in high-performance computing, numerical methods, algorithms, and software design now enable scientists and engineers to solve renewable energy business problems that were once thought of as intractable. Renewable energy resources and clean energy alternatives are playing a larger role in diversifying the world's energy future. The aim is to identify computational needs and opportunities in energy. The Issue addresses the challenges presented to the scientific and engineering communities by the expanding role of computational modeling and simulation. The production of observational big data for renewable energy promotes computational and data-enabled science and engineering in energy systems.
This Special Issue mainly aims to honour the issues supported by computational and data-enabled science and engineering in energy systems and energy security and hurricane disaster resilience for Florida's power system STINT grant and workshop. This Special Issue covers several topics, as those below:
- Computational, mathematical, and statistical methods in the renewable energy industry
- Visualization and mining tools in the renewable energy industry
- Large-scale simulations and analysis of large and heterogeneous collections of data in the renewable energy industry
- Cyber-physical systems in the renewable energy industry
- Data assimilation, state estimation, and control systems in the renewable energy industry
- Machine learning in the renewable energy industry
- Big data-enabled applications in the renewable energy industry
Assoc. Prof. Bahri Uzunoğlu
Guest Editor
Manuscript Submission Information
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Keywords
- Computational, mathematical, and statistical methods in the renewable energy industry
- Visualization and mining tools in the renewable energy industry
- Large-scale simulations and analysis of large and heterogeneous collections of data in the renewable energy industry
- Cyber-physical systems in the renewable energy industry
- Data assimilation, state estimation, and control systems in the renewable energy industry
- Machine Learning in the renewable energy industry
- Big Data-enabled applications in the renewable energy industry
