Empowering Resilient Operation of Smart Energy System with Renewables Using Artificial Intelligence-Based Data Analysis
摘要截稿:
全文截稿: 2024-12-31
影响因子: 4.937
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 3区
• 小类 : 能源与燃料 - 3区
Overview
As the global emphasis on renewable energy sources continues to grow, there's an imperative need for efficient and resilient operation of energy systems that can integrate these sources. With the convergence of artificial intelligence (AI) and energy systems, innovative data analysis techniques offer the potential to harness the benefits of renewables while ensuring system resilience. The integration of AI not only aids in predicting and managing the variabilities of renewable sources but also brings forward methods to optimize the operation, ensuring a stable energy supply. This special issue aims to delve into the AI-based methodologies, tools, and practices that empower the resilient operation of smart energy systems with renewables.
The topics of interest include, but are not limited to:
AI-driven predictive models for renewable energy output forecasting.
Techniques for resilience enhancement of energy systems using AI-based data analysis.
Energy management and optimization strategies leveraging AI.
Handling uncertainties and variabilities in renewable energy sources using AI.
Cybersecurity challenges and solutions in AI-integrated energy systems.
Data-driven control mechanisms for renewable integration.
AI-enabled tools for real-time monitoring and fault detection in energy systems.
Demand response and load forecasting in renewable-rich energy networks.
AI-based methods for energy storage and distribution in renewable-integrated systems.
Digital twin applications in smart energy systems powered by AI analytics.
Integration of AI with renewable microgrids for enhancing system resilience.
AI techniques for ensuring grid stability amidst high renewable penetration.
Deep learning methods for pattern recognition and anomaly detection in renewable energy systems.
Applications of federated learning in smart energy systems for data privacy and distributed learning.
Both review articles and original research articles are welcome.
Guest editors:
Managing Guest Editor
Dr. Yang LiSchool of Electrical Engineering, Northeast Electric Power University, China
Co-Guest Editors
Dr. Shunbo LeiSchool of Science and Engineering, The Chinese University of Hong Kong – Shenzhen, China
Dr Yifan ZhouDepartment of Electrical and Computer Engineering, Stony Brook University, USA
Manuscript submission information:
We sincerely welcome manuscripts containing novel, high quality, and unpublished research results. The invited submissions will be processed and reviewed in the same way as open submissions. Original papers on these topics and short reviews are welcome for submission. All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers.
Manuscript should be submitted via journal online submission system at: Editorial Manager by selecting the Article Type of " VSI: Smart Energy System". A detailed submission guideline is available as “Guide for Authors”.
Once your manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Important Dates:
Submission Open Date: 31 August 2024
Manuscript Submission Deadline: 31 December 2024