Call for Papers- SI on UDLSTSG

Special Issue on Utilizing Deep Learning and Statistical Theory in the Context of Smart Grids

Aims and Scope

With the proliferation of smart meters, sensors, and monitoring devices, smart grids generate an enormous amount of data. Traditional statistical methods often face limitations when dealing with high-dimensional, heterogeneous, and unstructured data. Deep learning techniques, on the other hand, excel at extracting intricate patterns and representations from big data. By applying deep learning and statistical theory, researchers and practitioners can unlock valuable information hidden within the smart grid data, leading to more informed decision-making processes. Its potential for enhancing the performance of smart grid systems has attracted interest from industry stakeholders, including power utilities, technology providers, and policymakers.

Enhancing the interpretability of deep learning requires a framework that deeply integrates domain knowledge in power applications with deep learning models. Meanwhile, probability theory, with its rigorous mathematical foundation, can be applied to deep learning applications as loss functions, evaluation criteria, and more. This holds significant academic significance for promoting the development of deep learning application theories.

The special issue intends to contribute to the advancement of knowledge and understanding of how statistical machine learning techniques can enhance the efficiency, reliability, and sustainability of smart grid systems.

Lead Guest Editor

Asso. Prof. Xueqian Fu Email:
China Agricultural University, China 
Research Interests: Statistical Machine Learning, Agricultural Energy Internet

Guest Editors

Dr. Xiaodong Zheng Email:
Southern Methodist University, USA
Research Interests: Robust Optimization, Distributed Optimization, Game Theory, Statistical Learning

Dr. Xiurong Zhang Email:
China Agricultural University, China
Research Interests: Wireless Communications Theory, Wireless Communications Systems, the Application of artificial intelligence in the Internet of Things for Fisheries

Dr. Zhenjia Lin Email:
The Hong Kong Polytechnic University, Hong Kong SAR, China
Research Interests: Uncertainty Optimization, Data-analysis for Power System Applications


Special Issue Information
The contributions from researchers describing their original, unpublished, research contribution on the following theme (but not limited to):

  • Deep learning models for renewable energy generation
  • Probability theory for integration of renewable energy generation
  • Optimization of energy scheduling and demand response using advanced statistical analytics
  • Uncertainty quantification in smart grid operations
  • Integration of artificial intelligence techniques for grid resilience and cybersecurity
  • Energy management and control using deep learning approaches
  • Application of artificial intelligence in the fishery energy internet
  • Case studies, applications, and real-world implementations of statistical machine learning in smart grids.

Manuscript Submission Information
Submission deadline: January 31, 2024
Submissions that pass pre-check will be reviewed by at least two reviewers of the specific field.
Free fast publication and early access will be provided to all accepted papers. 

If you have any queries regarding this special issue or other matters, please feel free to contact the editorial office: