Application of Advanced Machine/Deep Learning in Energy Economics, Management, and Sustainability
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 (31 August 2023) | Viewed by 3994
Special Issue Editors
Interests: microgrid; smart grid; interoperability; deep reinforcement learning
Interests: power system optimization; AI and machine learning application in power system; deep learning; IoT
Interests: AI and machine learning; deep learning; image processing and NLP; big data IOT
Special Issues, Collections and Topics in MDPI journals
Interests: internet of things; blockchain; machine learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent decades, electrical power systems have been more vulnerable than before, mainly due to grid modernization and the high penetration of renewable energies. Moreover, smart sensors, e.g., Internet of Things-based devices, have been integrated into the network that generate a huge amount of data, which can cause networks to be more prone to cyber-attacks. Therefore, advanced techniques and technologies are required to detect and mitigate attacks, as well as take advantage of these data to increase the reliability, resiliency, sustainability, and efficiency of the entire system.
On the other hand, machine/deep learning techniques have proven their high capability in data processing and classification. Indeed, by using advanced artificial intelligence techniques, we can have real-time processing of the data to predict unusual events in advance. This can help the operators not only in real-time monitoring and managing of the system to prevent any severe blackout, but also to increase the sustainability of the network. These techniques also have many real-time applications in decision making (e.g., artificial intelligence-based reconfiguration and artificial intelligence-based fault detection and protection), forecasting (e.g., weather, wind turbine output power, and solar output power), and monitoring (e.g., artificial intelligence-based voltage monitoring and artificial intelligence-based generator speed limit monitoring) of the large scale electrical power grids and smart grids/cities. However, these techniques need strong justification and investigation before formal adoption to the grids.
The aim of this Special Issue is to investigate the application of advanced machine/deep learning techniques in electrical power management, economic development, and sustainability.
Topics:
- Application of machine/deep learning in cyber-attack detection and mitigation;
- Application of AI enabled IoT and blockchain in grid security and reliability;
- Application of artificial intelligence in energy management;
- Artificial intelligence-based monitoring and protection;
- Artificial intelligence-based anomaly detection in electrical power and smart grids/cities;
- Integration of machine/deep learning and advanced technologies in energy systems;
- Applications of machine learning in modelling and forecasting.
Dr. Lilia Tightiz
Dr. Aliasghar Baziar
Dr. Amin Sahba
Dr. Shabir Ahmad
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. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- machine learning
- deep learning
- IoT
- sustainability
- blockchain
- cyber attack
- energy economics
- artificial intelligence
- remedial action schema
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