Many countries and organizations have proposed smart city projects to address the
exponential growth of the population by promoting and developing a new paradigm for …

In this paper, we study radio and energy resource management in renewable energy-
powered wireless networks, where base stations (BSs) are powered by both on-grid and …

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for
autonomous control and integration of renewable energy resources into smart power grid …

The prevalence of the Internet of things (IoT) and smart meters devices in smart grids is
providing key support for measuring and analyzing the power consumption patterns. This …

The integration of Internet of Things (IoT) technology with deep reinforcement learning
(DRL) has emerged as a transformative approach in the realm of smart grid management …

The power consumption of households has been constantly growing over the years. To cope
with this growth, intelligent management of the consumption profile of the households is …

In order to overcome the limitation of traditional reinforcement learning techniques on the
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …

Energy management in the smart home can help reduce residential energy costs by
scheduling various energy consumption activities. However, accurately modeling factors …

The future communities are becoming more and more electrically connected via increased
penetrations of behind-the-meter (BTM) resources, specifically, electric vehicles (EVs), smart …

Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing
numbers of household users with controllable loads to actively participate in smart grid …