A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Towards next generation virtual power plant: Technology review and frameworks
Modernization in the area of smart energy equipment, are forming the perception of an
interlinked energy network in subsequent times. This energy network is contemplated to …
interlinked energy network in subsequent times. This energy network is contemplated to …
[HTML][HTML] A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems
Different fields have been thriving with the advents in mobile communication systems in
recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next …
recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next …
Emerging trends and approaches for designing net-zero low-carbon integrated energy networks: A review of current practices
The incorporation of net-zero technology into preexisting energy networks is crucial for
facilitating the shift toward an ecologically conscious and sustainable energy infrastructure …
facilitating the shift toward an ecologically conscious and sustainable energy infrastructure …
Privacy preserving load control of residential microgrid via deep reinforcement learning
Demand side management has been proved to be effective in improving the operating
efficiency of microgrids, while posing a severe threat to user privacy. This paper proposes a …
efficiency of microgrids, while posing a severe threat to user privacy. This paper proposes a …
Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning
J Wang, C Guo, C Yu, Y Liang - Electric power systems research, 2022 - Elsevier
Virtual power plants (VPPs), which aggregate customer-side flexibility resources, provide an
effective way for customers to participate in the electricity market, and provide a variety of …
effective way for customers to participate in the electricity market, and provide a variety of …
[HTML][HTML] AI agents envisioning the future: Forecast-based operation of renewable energy storage systems using hydrogen with Deep Reinforcement Learning
A Dreher, T Bexten, T Sieker, M Lehna, J Schütt… - Energy Conversion and …, 2022 - Elsevier
Hydrogen-based energy storage has the potential to compensate for the volatility of
renewable power generation in energy systems with a high renewable penetration. The …
renewable power generation in energy systems with a high renewable penetration. The …
[HTML][HTML] A taxonomy of machine learning applications for virtual power plants and home/building energy management systems
A Virtual power plant is defined as an information and communications technology system
with the following primary functionalities: enhancing renewable power generation …
with the following primary functionalities: enhancing renewable power generation …
AEBIS: AI-enabled blockchain-based electric vehicle integration system for power management in smart grid platform
A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible
power consumers, and storage systems. A VPP balances the load on the grid by allocating …
power consumers, and storage systems. A VPP balances the load on the grid by allocating …