Machine Learning Techniques for Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: closed (30 September 2020)
Special Issue Editor
Interests: controls systems; energy systems (fuel and solar cells, wind, and smart grids); wireless communications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of computational power in the last ten years, machine learning techniques based on the neural networks of the past have been given new life since they now can be studied and utilized using many layers. This has led to the discovery of so-called deep learning techniques that have numerous potential applications to many real physical and/or artificial (manmade) systems. The use of machine learning technique in all areas of science and engineering appears to be the imperative of modern times, especially in the past three to four years. Supervised and unsupervised machine learning techniques can be used to solve numerous classification, regression, and clustering problems. Various machine learning techniques (convolutional neural networks, spiking neural networks, support vector machines, reinforcement learning, etc.) have already been utilized to improve the processes and operations of energy systems. It is important to emphasize that the reinforcement learning technique represents an approximate dynamic programming method—the method that together with the calculus of variations was used to develop algorithms for the optimal control of dynamic systems.
Although many journal papers have been published on the use of machine learning in energy systems, a lot of research opportunities still exist in this area. The time has come for researchers in energy systems to systematically use machine learning techniques and algorithms to make energy systems more efficient, better controlled and stabilized, better managed, more ecologically friendly, more user friendly, and provide guidelines for how to build future energy systems for various applications in diverse engineering and scientific fields.
Prof. Dr. Zoran Gajic
Guest Editor
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Keywords
- machine learning
- neural networks
- deep learning
- reinforcement learning
- support vector machines
- energy systems (solar, wind, fuel cells, power systems, smart grids, batteries, ultra-capacitors, thermal energy, nuclear energy, tidal energy, wave energy, magnetic energy)
- applications
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