With rising energy costs and concerns about environmental sustainability, there is a growing
need to deploy Home Energy Management Systems (HEMS) that can efficiently manage …

Microgrids have emerged as a key element in the transition towards sustainable and
resilient energy systems by integrating renewable sources and enabling decentralized …

Microgrids create conditions for efficient use of integrated energy systems containing
renewable energy sources. One of the major challenges in the control and operation of …

This paper utilizes a fully model-free and data-driven deep reinforcement learning (DRL)
framework to develop an intelligent controller that can exploit information to optimally …

Today, with the increase in the integration of renewable sources, the home energy
management system (HEMS) has become a promising approach to improve grid energy …

The demand response (DR) program of a traditional home energy management system
(HEMS) usually controls or schedules appliances to monitor energy usage, minimize energy …

The hereby study combines a reinforcement learning machine and a myopic optimization
model to improve the real-time energy decisions in microgrids with renewable sources and …

The plugging and unplugging of high-power loads result in dynamically changing power
demands for islanded microgrids. However, the existing distributed real-time dispatch …

This paper proposes a model-free self-supervised learning (SSL) method for dispatching
distributed energy resources (DERs). The proposed method first establishes a data-driven …

This paper proposes a novel parallel hybrid deep reinforcement learning (DRL) approach to
address the real-time energy management problem of microgrid. Since the proposed …