Use of Intelligent Predictive Analytics and Cyber-Physical Mechanisms for Smart Grid Management and Control

Session Chairs

Assoc. Prof. Dr. Tan Chin Ike
School of Computing
Asia Pacific University

Assoc. Prof. Dr. Muhammad Ehsan Rana
School of Computing
Asia Pacific University

Aim and Scope:

A smart grid uses state-of-the-art computing, communication, control, and information technologies for improved security, efficiency, stability, and sustainability. Energy Internet combines cyber-physical and social systems, integrates multiple energy systems, and creates innovative modes of business for the whole energy system. Unfortunately, integrating advanced control may create vulnerabilities in the physical grid, which adversaries can exploit via data manipulation and false data injection to compromise the grid’s safety. Therefore, it is necessary to understand the fundamentals and engineering aspects of cyber-physical mechanisms prone to critical failures. Sensors enable the extensive collection of monitoring data that can help better understand the workings of smart grids. The storage of tremendous volumes of raw data to provide substantial support for precise data analytics becomes difficult. Further, due to undependable network transfers, these enormous volumes of data likely contain missing values or may have outliers. Meanwhile, data analytics and machine learning techniques have been considerably developed recently. It is essential to find out the application of such state-of-the-art methods for improving the energy infrastructure. Although increasing efforts are being made in modeling, analyzing, controlling, operating, and planning smart grids from a data-driven perspective, it is believed that the research on data analytics and machine learning still has a long way to go in the field of the energy internet. This special issue aims to address the critical areas of cyber-physical security of the smart grid and the importance of predictive analytics.


This special issue invites research papers from the following topics (but not limited) to further recognize the importance of cyber-physical systems for smart grids using intelligent predictive analytics. 

• Applications and challenges of a wireless sensor network in a smart grid 

• Novel optimal placement of measurement devices for state estimation in smart grid • Big data issues and analytics in smart grid 

• Probabilistic load flow calculations for smart grid power systems 

 • Intelligent grid architecture for energy security 

• Cybersecurity awareness, standards, and regulatory frameworks • Guidelines for smart grid cyber security 

 • Predictive analytic frameworks for cyber-physical smart grid systems 

• Development of control strategies for smart grid systems 

• Smart meter data analytics 

• Security of cyber-physical systems 

• Efficient energy management of smart grid cyber-physical systems 

• Cyber-physical system deployment in smart cities 

• Predictive analytics-based smart microgrid control 

• Cyber supply chain risk management 

• Enhancing cyber-physical system resilience 

• Case studies on IoT-driven smart microgrids for rural electrification