Data-Protection Combined with Machine Learning for AI-Integrated Smart Power Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 9754
Special Issue Editors
Interests: fault diagnosis; prognosis; control; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: predictive maintenance; FIDR; machine learning; artificial intelligence; signal processing; formal verification; model checking
Special Issues, Collections and Topics in MDPI journals
Interests: automatic control; power electronic systems; time-delay systems; renewable energy systems; robotics
Special Issue Information
Dear Colleagues,
Due to the availability of sophisticated information and communication technology, it has become viable to collect data from a single component or from a whole system, which can be used later to gain valuable insights for smart decision making. The applications of data-driven decision-making can be found in numerous fields, including medicine, finance, transportation, industrial setup, etc. Data-driven intelligent decision making is also applicable in the energy sector. With the availability of a wide range of sensors and advanced algorithms of artificial intelligence, it is possible to develop strategies that can improve monitoring, efficiency, reliability, and control of energy systems. The devised strategies might just consist of simple statistical analysis of the data to perform a cost–benefit analysis and forecasting or to address a technical issue, for instance, fault detection diagnosis, and prognosis of energy systems.
This Special Issue aims to present original research papers with high quality and novelty and also review papers on “Data Analytics in Energy Systems”.
Topics of interest include but are not limited to:
- Data analytics for energy system operation and control;
- Multimodal data analytics and fusion;
- Distributed data mining;
- Artificial intelligence, machine learning, and deep learning for energy systems;
- Cloud computing for data analytics in energy systems;
- Data analytics for energy demand forecasting;
- Data collection, visualization, statistical analysis, storage, and information management in energy systems;
- Fault detection, diagnosis, and prognosis methodologies;
- Model and fuzzy-based control design for smart power systems.
Dr. Farzin Piltan
Dr. M. M.Manjurul Islam
Dr. Belem Saldivar
Guest Editors
Manuscript Submission Information
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Keywords
- Artificial intelligence
- Automation
- Big data
- Deep learning
- Data analytics
- Data processing
- Fault detection and diagnosis
- Energy Systems
- Machine learning
- System operation and control
- Smart grid
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