Machine Learning and Artificial Intelligence into Analysis, Control, and Applications of Renewable Energy Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".
Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 497
Special Issue Editors
Interests: power electronics; renewable energy; energy conversion; power system; smart grid; motor control; electric vehicle; reinforcement learning; deep learning
Interests: algorithms; data science; bioinformatics and computational biology; combinatorial optimization
Interests: power systems; renewable energy; distributed energy resources; smart grid; power electronics; cyber–physical power system; grid control and optimization
Special Issue Information
Dear Colleagues,
Nowadays, renewable sources of energy such as wind power, solar energy, etc., are playing an increasingly critical role in creating a greener environment. Power converters are key components that physically connect wind power, solar panels, and batteries to the grid. Many applications for Wide Band Gap (WBG) semiconductor devices such as SiC and GaN, are also being seen more often in renewable energy fields.
In recent years, machine learning, also known as artificial intelligence, has succeeded in the image processing and language processing fields, among others. These of these technologies have had several successful applications in the power and renewable energy fields, such as for the controlling of the power converters and solar forecasting, etc.
This Special Issue intends to seek interdisciplinary topics at the junction between renewable energies and computer science, especially in artificial intelligence or machine learning.
Topics of interest include, but are not limited to:
- The application of power electronics applications in renewable sources of energy;
- Wide Band Gap (WBG) semiconductor devices such as SiC and GaN in renewable sources of energy;
- Wind forecasting/power using machine learning, deep learning, and artificial intelligence;
- Solar forecasting/power using machine learning, deep learning, and artificial intelligence;
- Applications of renewable sources of energy such as solar and wind in computer science fields such as bioenergy, bioinformatics, etc.
- Solar home systems;
- Building Energy Management (BEM);
- Demand response for grid stability and resiliency;
- Net-zero energy buildings;
- Distributed Energy Management Systems (DERMS).
Dr. Xingang Fu
Dr. Letu Qingge
Dr. Abdullah Al Hadi
Guest Editors
Manuscript Submission Information
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Keywords
- wind power
- solar photovoltaic
- neural networks
- machine learning
- deep learning
- artificial intelligence
- Wide Band Gap (WBG) semiconductors
- SiC
- GaN
- forecasting
- distributed energy resources
- smart grids
- energy
- demand response
- power converters
- renewable energy
