Challenges and Research Trends of AI Methods for IoT Enabled Renewable Energy Systems
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (30 November 2022)
Special Issue Editors
Interests: Renewable energy systems; Internet of Things; machine/deep learning; data mining/data science; optimization techniques; climate prediction using ML; smart transportation; smart/precision agriculture; smart cities
Interests: network optimization; Internet of Things; machine/deep learning; wireless sensor networks; smart transportation; smart/precision agriculture; smart cities; optimization techniques
Interests: cyber security; applied AI; IoT security and privacy; key agreement; body area networks; blockchains
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Renewable energy systems (RES) have attracted many researchers due to the sustainability issues and the requirement for climate control/protection in the last decades. However, the intermittent nature of renewable energy sources causes a radical change in the steadiness of energy systems, and causes extensive load and pressure on electrical systems. To carefully optimize these systems, artificial intelligence methods were extensively used to deal with optimization issues in several areas, recently, in renewable energy systems.
Alternatively, several innovative methods, e.g., deep/machine learning, evolutionary algorithms, optimization techniques, big data in conjunction with cutting-edge technologies, such as the Internet of Things (IoT), have been used to increase the stability, consistency, resiliency, and efficiency of renewable energy systems. Hence, the objective of this Special Issue is to explore the use of cutting-edge technologies in conjunction with AI methods in renewable energy networks.
The main topics of the issue include, but are not limited to, the following:
Prof. Zahoor Jan
Dr. Masood Ahmad
Dr. Aftab Ali
Guest Editors
Manuscript Submission Information
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Keywords
- Application of machine learning and optimization methods in renewable-based micro-grids and smart grids/cities
- Application of machine learning and optimization methods in renewable-based transportation modeling and control
- Smart charging methods for vehicle-to-grid (V2G) communication and control that helps in maintaining a green world
- AI methods for monitoring and characterization of IoT-enabled renewable energy systems
- Network architectures and platforms for IoT-enabled renewable energy systems
- Sensors and devices for IoT-enabled renewable energy systems
- Edge computing and cloud computing for IoT-enabled renewable energy systems
- Artificial intelligence and machine learning applied in IoT-enabled renewable energy systems
- Energy harvesting for IoT-enabled renewable energy system applications
- Smart city and smart building applications using IoT-enabled renewable energy systems
- Smart agricultural and environmental applications of IoT-enabled renewable energy systems
- Outdoor and indoor applications of IoT-enabled renewable energy systems
- Business model and cost analysis for IoT-enabled renewable energy systems.
