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 …

Smart energy performance monitoring and optimisation at the supplier and consumer levels
is essential to realising smart cities. In order to implement a more sustainable energy …

Smart energy performance monitoring and optimisation at the supplier and consumer levels
is essential to realising smart cities. In order to implement a more sustainable energy …

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) …

Smart meter measurements, though critical for accurate demand forecasting, face several
drawbacks including consumers' privacy, data breach issues, to name a few. Recent …

In the contemporary energy landscape, power generation comprises a blend of renewable
and non-renewable resources, with the major supply of electrical energy fulfilled by non …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Federated learning (FL) is a collaborative, decentralized privacy‐preserving method to
attach the challenges of storing data and data privacy. Artificial intelligence, machine …

In this paper, we propose privacy-friendly electricity consumption prediction models based
on Federated Learning (FL). Federated Learning provides a novel framework for Artificial …

With the deployment of smart sensors and advancements in communication technologies,
big data analytics have become vastly popular in the smart grid domain, informing …