Machine Learning and Data Mining Applications in Power and Multi-Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 58026
Special Issue Editors
2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Interests: power quality; electrical power engineering; renewable energy technologies; machine learning; clean energy; multi-energy systems; data mining; sustainable development
Special Issues, Collections and Topics in MDPI journals
2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Interests: signal analysis; advanced signal processing methods; renewable energy; ecology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is intended as a forum for advancing research and for applying machine learning and data mining in order to facilitate the development of modern electric power systems, grids and devices, smart grids, and protection devices, as well as for developing tools for more accurate and efficient power system analysis and multi-energy systems.
Conventional signal processing is not more adequate for extracting all of the relevant information from distorted signals through filtering, estimation, and detection in order to facilitate decision making and to control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data mining statistical signal detection, and estimation may help in solving contemporary challenges in modern power systems and multi-energy systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; dynamic optimization of grid operations; demand response; incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information and to transform information into actionable intelligence.
The expected outcomes will be a grid with improved situation awareness, faster and more accurate control actions to detect and isolate faults, improved assurance of power quality, and higher levels of energy efficiency.
Dr. Michał Jasiński
Prof. Dr. Zbigniew Leonowicz
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- data mining
- smart grids
- power system control
- power system protection
- power flow
- energy management
- renewable energy
- demand-side management
- demand response
- load scheduling
- uncertainty estimation
- power balancing
- multi-energy systems
- planning and operation of energy hubs
- P2X technologies
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