Reinforcement learning and its applications in modern power and energy systems: A review
With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …
other emerging technologies, there are increasing complexities and uncertainties for …
[HTML][HTML] Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …
Transfer learning in deep reinforcement learning: A survey
Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …
problems. Recent years have witnessed remarkable progress in reinforcement learning …
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 building controls: The opportunities and challenges
Building controls are becoming more important and complicated due to the dynamic and
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
Multi-agent deep reinforcement learning for HVAC control in commercial buildings
In commercial buildings, about 40%-50% of the total electricity consumption is attributed to
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
[HTML][HTML] Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …
systems by extracting value from the data generated by the deployed metering and sensing …
A multi-agent reinforcement learning-based data-driven method for home energy management
This paper proposes a novel framework for home energy management (HEM) based on
reinforcement learning in achieving efficient home-based demand response (DR). The …
reinforcement learning in achieving efficient home-based demand response (DR). The …
Review on the research and practice of deep learning and reinforcement learning in smart grids
D Zhang, X Han, C Deng - CSEE Journal of Power and Energy …, 2018 - ieeexplore.ieee.org
Smart grids are the developmental trend of power systems and they have attracted much
attention all over the world. Due to their complexities, and the uncertainty of the smart grid …
attention all over the world. Due to their complexities, and the uncertainty of the smart grid …
Model-free real-time EV charging scheduling based on deep reinforcement learning
Driven by the recent advances in electric vehicle (EV) technologies, EVs have become
important for smart grid economy. When EVs participate in demand response program which …
important for smart grid economy. When EVs participate in demand response program which …