Motivated by present and forthcoming challenges in the adoption and integration of
distributed renewable energy, we develop a machine learning (ML) approach that builds …

Twenty-first century technology is driving profound changes in the electric utility business
model. At the same time, the threat of global climate change necessitates the adoption of …

The continuously evolving landscape of the modern electric grid has equipped traditionally
passive power elements with communication and intelligent actuation capabilities. This …

Increased demand for fast edge computation and privacy concerns have shifted researchers'
focus towards a type of distributed learning known as federated learning (FL). Recently …

Renewable energy is an environment-friendly and economically attractive source of
electricity generation. However, substantial grid integration of renewable energy is …

As the energy landscape evolves toward sustainability, the accelerating integration of
distributed energy resources poses challenges to the operability and reliability of the …

The successful implementation of Renewable Energy Communities (RECs) involves
maximizing the self-consumption within a community, particularly in regulatory contexts in …

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon
neutrality as we grapple with climate change. With deepening penetration of renewable …

In traditional deep learning algorithms, one of the key assumptions is that the data
distribution remains constant during both training and deployment. However, this …

Clustering of customer consumption (load) patterns, known as load demand user profiling, is
a technique widely used by utility companies. Load demand user profiling facilitates tasks …