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Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Hybrid policy-based reinforcement learning of adaptive energy management for the Energy transmission-constrained island group
This article proposes a hybrid policy-based reinforcement learning (HPRL) adaptive energy
management to realize the optimal operation for the island group energy system with energy …
management to realize the optimal operation for the island group energy system with energy …
[HTML][HTML] Applications of reinforcement learning in energy systems
Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …
renewable energy technologies and improve efficiencies, leading to the integration of many …
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 …
A multi-agent deep reinforcement learning method for cooperative load frequency control of a multi-area power system
This paper proposes a data-driven cooperative method for load frequency control (LFC) of
the multi-area power system based on multi-agent deep reinforcement learning (MA-DRL) in …
the multi-area power system based on multi-agent deep reinforcement learning (MA-DRL) in …
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
Distributed economic dispatch in microgrids based on cooperative reinforcement learning
Microgrids incorporated with distributed generation (DG) units and energy storage (ES)
devices are expected to play more and more important roles in the future power systems …
devices are expected to play more and more important roles in the future power systems …
Reinforcement learning for electric power system decision and control: Past considerations and perspectives
In this paper, we review past (including very recent) research considerations in using
reinforcement learning (RL) to solve electric power system decision and control problems …
reinforcement learning (RL) to solve electric power system decision and control problems …
Indirect multi-energy transactions of energy internet with deep reinforcement learning approach
With the new feature of multi-energy coupling and the advancement of the energy market,
Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated …
Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated …