Table of Content

Towards Big Data Analytics: Smart and Intelligent Techniques for Sustainable Smart Grid

Submission Deadline: 30 June 2022 (closed)

Guest Editors

Dr. Tariq Ali, COMSATS University Islamabad, Pakistan.
Dr. Muhammad Awais, Lancaster University, United Kingdom.
Dr. Mohammad Maroof Siddiqui, Dhofar University, Oman.


The proposed special issue will cover advanced research in the price and load forecasting smart and intelligent techniques in smart grid. It will also cover the theft detection and false data attacks in the overhead transmission line and energy-efficient system design for smart grids. Smart grids are very popular and have applications in smart cities. The smart grids are not only the power networks; rather they serve the people and businesses in many ways. To carry out all these functions, a lot of measurements are required in the smart grids. Similarly, a lot of control and monitoring-related tasks are essential for the smooth operation of smart grids. The optimization in power systems is essential in smart cities applications and it is vital to keep power systems in operation by detecting and diagnosing various faults at early stages. For the smart city design, the Data-driven-based pricing and incentive schemes could be the first step towards a fully integrated IoT strategy that optimizes productivity and ultimately realizes cost-saving goals. Smart AI and IoT-based solutions could be adopted to reduce the plant shutdowns and associated maintenance costs.

In this context, this Special Issue aims to publish novel research work and visionary reviews on advanced smart grid analytics technologies, algorithms, vibrating sensing technologies, case studies, and their associated applications in the smart grid, power grid modernization, and smart energy trading systems.


Potential topics include but are not limited to the following:
• Smart grid analytics for electricity theft detection
• Smart grid analytics for power system resilience
• Smart grid analytics for demand-side management, and load forecasting
• Smart grid analytics for demand-side management, and customer behavior analytics
• Data-driven-based pricing and incentive schemes and protocols
• Cloud computing, and edge-based sustainable smart grid, and smart cities
• Condition monitoring of power systems in smart grid
• IoT based intelligent system design in smart grid
• AI and ML-based data attacks and energy theft detection
• Power system load forecasting in smart grid
• Demand-side management and demand response
• Application of artificial intelligence, IoT, and big data analytics in power networks
• Load forecasting and scheduling in smart grid
• Big data and smart grid analytics in smart cities
• vibrating sensing technologies

Published Papers

  • Open Access


    Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

    Muhammad Irfan, Nasir Ayub, Faisal Althobiani, Zain Ali, Muhammad Idrees, Saeed Ullah, Saifur Rahman, Abdullah Saeed Alwadie, Saleh Mohammed Ghonaim, Hesham Abdushkour, Fahad Salem Alkahtani, Samar Alqhtani, Piotr Gas
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2141-2158, 2022, DOI:10.32604/cmc.2022.025466
    (This article belongs to this Special Issue: Towards Big Data Analytics: Smart and Intelligent Techniques for Sustainable Smart Grid)
    Abstract One of the major concerns for the utilities in the Smart Grid (SG) is electricity theft. With the implementation of smart meters, the frequency of energy usage and data collection from smart homes has increased, which makes it possible for advanced data analysis that was not previously possible. For this purpose, we have taken historical data of energy thieves and normal users. To avoid imbalance observation, biased estimates, we applied the interpolation method. Furthermore, the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing. By proposing an improved version… More >

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