The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in
smart cities represents a pivotal advancement in urban innovation. This convergence …

Impressive technological advancements over the past decades commenced significant
advantages in the maritime industry sector and elevated commercial, operational, and …

With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …

Significant amounts of data are collected in buildings. While these data have great potential
for maximizing the energy efficiency of buildings in general, only a small portion of the data …

Data aggregation plays a crucial role in smart grid communication as it enables the
collection of data in an energy-efficient manner. However, the widespread deployment of …

Non-intrusive load monitoring is a prominent part of demand-side energy management that
provides visibility of flexible loads to support real-time electricity market pricing strategies …

Differential privacy (DP), as a promising privacy-preserving model, has attracted great
interest from researchers in recent years. At present, research on the combination of deep …

While smart grid significantly facilitates energy efficiency by using users' power consumption
data, it poses privacy leakage risk for user personal behaviors. Differential privacy (DP) has …

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and
is effective in disaggregating smart meter readings from the household-level into appliance …

The proliferation of the Internet of Things (IoT) has transformed the digital landscape,
enabling a vast array of interconnected devices to communicate and share data seamlessly …