Sustainable Energy Systems: From Grid Efficiency Through AI-Based Solutions to Energy Communities
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 2615
Special Issue Editors
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues, Collections and Topics in MDPI journals
Interests: big data; bioinformatics; computational intelligence; data science; energy monitoring and management; intelligent transportation; optimization; semantic web
Special Issues, Collections and Topics in MDPI journals
2. Effat College of Business, Effat University, Jeddah, Saudi Arabia
Interests: smart cities; migration; innovation networks; international business; political economy; economic integration; politics; EU; Central Europe; China
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Global warming is one of the top global challenging issues that must be addressed. Today’s smart city era facilitates the migration of traditional electric grids to smart grids. The smart grid infrastructure, attached to massive numbers of sensors, is scalable and flexible to collect and store data, which is further transformed and analyzed to provide valuable insights into energy efficiency via artificial intelligence techniques.
With the rapid development of computing infrastructure, tools, and techniques such as cloud computing, fog computing, edge computing, graphics processing units, and deep learning, the performance of many smart grid applications could be further optimized. In general, we can move forward in smart grids from two different points of view:
(i) Electric utility: How to introduce more green energy? How to reduce the electricity wastage between generated and actual consumed electricity? How to predict the energy demand (including peak demand) accurately? Is there a recommendation on optimal operation management?
(ii) End users: What is the energy usage profile of each electric appliance? How to reduce energy usage in power-hungry electric appliances?
This Special Issue will report high-quality research on artificial intelligence in the fields of smart grid, and, more specifically, on state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of these convergence technologies for providing insights towards energy science and engineering. Key topics include but are not limited to the following:
- New theories and applications of artificial intelligence in smart grids;
- Multiobjective optimization in computationally expensive optimization problems;
- Multiobjective optimization in bi-level optimization problems;
- Energy disaggregation techniques in non-intrusive load monitoring;
- Smart grids for smart energy management in smart cities;
- Migration of traditional electric grids to smart grids;
- Modelling and simulation (or co-simulation) in smart grids;
- Demand response in smart grids;
- Smart grid solutions for rural areas, that is, smart transmission grids;
- Modelling and simulation (or co-simulation) in smart grids;
- Internet of Things and smart grids;
- Fraud detection and predictive maintenance;
- Demand response in smart grids;
- Peak load management approaches in smart grids;
- Cloud, fog, edge computing techniques in smart grids;
- Smart grid solutions for rural areas;
- Artificial intelligence policy challenges and management;
- Social research in smart grids;
- Case studies and pilot studies;
- Evidence-based policy-making in the domain of smart grids in rural and urban contexts.
Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Prof. Anna Visvizi
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- energy efficiency
- machine intelligence
- machine learning
- optimization algorithms
- smart cities
- smart grids
- grid efficiency optimization: conventional and AI-enhanced methods
- energy communities
- prosumers
- new "renewables" and the EU, including nuclear energy
