Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions
It provides a comprehensive overview for researchers and practitioners in energy systems, but is incremental as it synthesizes existing knowledge without introducing new methods.
This paper surveys the architectural elements of smart grids and reviews existing machine learning and deep learning applications for analyzing the large volumes of data they generate, while also identifying current research limitations and future directions.
The Smart grid (SG), generally known as the next-generation power grid emerged as a replacement for ill-suited power systems in the 21st century. It is in-tegrated with advanced communication and computing capabilities, thus it is ex-pected to enhance the reliability and the efficiency of energy distribution with minimum effects. With the massive infrastructure it holds and the underlying communication network in the system, it introduced a large volume of data that demands various techniques for proper analysis and decision making. Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights. This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid. In addition in terms of machine learning-based data an-alytics, this paper highlights the limitations of the current research and highlights future directions as well.