Given its advantages in low latency, fast response, context-aware services, mobility, and
privacy preservation, edge computing has emerged as the key support for intelligent …

The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …

Demand side management (DSM) has become one of the major concerns of the smart grids
to cope with the penetration of renewable energy. The availability of new communication …

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided
wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the …

The smart power grid is a critical energy infrastructure where real-time electricity usage data
is collected to predict future energy requirements. The existing prediction models focus on …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

With increasing concerns for data privacy and ownership, recent years have witnessed a
paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL) …

Edge-based technologies have emerged as a key enabler to empower low-latency services
and incorporate machine learning techniques for learning/inference. However, transferring …

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy
data, particularly when used to train machine learning models for different services. These …

Water consumption remains a major concern among the world's future challenges. For
applications like load monitoring and demand response, deep learning models are trained …