Special Issue "Artificial Intelligence for Smart and Sustainable Energy Systems and Applications"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 March 2019).
Prof. Miltiadis D. Lytras
1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi, Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Website | E-Mail
Interests: cognitive computing; artificial intelligence; data science; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; transportation; knowledge management; semantic web
Dr. Kwok Tai Chui
Earth has experienced rapid climate change and global warming is important to our future. The migration from electrical grid to smart grid has been one of the crucial areas in smart city. Smart grid offers attractive advantages like carbon emission reduction, energy saving via reduction consumption, better customer service, fraud detection and demand response. Its multidisciplinary nature motivates the need for innovative and robust solutions coming from different fields of knowledge. Attributed to the complexity and abundance of data, artificial intelligence plays an important role for the success of smart grid.
This special issue aims to consolidate recent advances in artificial intelligence for smart grid, research in theory and applications. Pilot study in smart grid is especially welcome. Topics of interest for the special issue include (but are not limited to)
- New theories and applications of machine learning algorithms in smart grid
- Design, development and application of deep learning in smart grid
- Artificial intelligence in advanced metering infrastructure
- Multiobjective optimization algorithms in smart grid
- Disaggregation techniques in non-intrusive load monitoring
- Modelling and simulation (or co-simulation) in smart grid
- Internet of Things and smart grid
- Data driven analytics (descriptive, diagnostic, predictive and prescriptive) in smart grid
- Artificial intelligence techniques for security
- Fraud detection and predictive maintenance
- Demand response in smart grid
- Peak load management approach in smart grid
- Interoperability in smart grid
- Cloud computing based smart grid
- Vehicle-to-grid design, development and application
Prof. Dr. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Manuscript Submission Information
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