Deep reinforcement learning (DRL) is decisive in addressing uncertainties in intelligent grid-
building interactions. Using DRL algorithms, this research optimizes the operational strategy …

In an effort to achieve energy efficiency, cost savings, and reduce carbon footprint,
researchers are exploring ways to strategically redistribute energy use in cooling systems …

Learning in high-dimensional action spaces is a key challenge in applying reinforcement
learning (RL) to real-world systems. In this paper, we study the possibility of controlling …

The challenges posed by renewable energy and distributed electricity generation motivate
the development of deep learning approaches to overcome the lack of flexibility of traditional …

With the growth of Renewable Energy (RE) generation, the operation of power grids has
become increasingly complex. One solution is automated grid operation, where Deep …

The topology optimization of transmission networks using Deep Reinforcement Learning
(DRL) has increasingly come into focus. Various researchers have proposed different DRL …

Air pollution that continues to increase can cause several health problems, one of which is
respiratory problems. Some of the air pollutants that are often found include particles or dust …

The growing demand for electricity, driven by widespread adoption of heat pumps, electric
vehicles, and industrial electrification, strains power grids and introduces challenges for a …

There has been an increasing interest on microgrids due to the CO2 emission crisis and the
considerable growth of renewable energy sources. There can exist multiple microgrids in a …