[HTML][HTML] Applications of reinforcement learning in energy systems
ATD Perera, P Kamalaruban - Renewable and Sustainable Energy …, 2021 - Elsevier
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 …
[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 …
[HTML][HTML] Reinforced model predictive control (RL-MPC) for building energy management
Buildings need advanced control for the efficient and climate-neutral use of their energy
systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two …
systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two …
On-line building energy optimization using deep reinforcement learning
Unprecedented high volumes of data are becoming available with the growth of the
advanced metering infrastructure. These are expected to benefit planning and operation of …
advanced metering infrastructure. These are expected to benefit planning and operation of …
Learning-based model predictive control for safe exploration
T Koller, F Berkenkamp, M Turchetta… - 2018 IEEE conference …, 2018 - ieeexplore.ieee.org
Learning-based methods have been successful in solving complex control tasks without
significant prior knowledge about the system. However, these methods typically do not …
significant prior knowledge about the system. However, these methods typically do not …
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …
learning (RL). To optimize driving performance, a reward function is developed by …
Reinforcement learning for control: Performance, stability, and deep approximators
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus
Hybrid electric vehicles offer an immediate solution for emissions reduction and fuel
displacement under the current technique level. Energy management strategies are critical …
displacement under the current technique level. Energy management strategies are critical …
On the guidance, navigation and control of in-orbit space robotic missions: A survey and prospective vision
BM Moghaddam, R Chhabra - Acta Astronautica, 2021 - Elsevier
In the first part, this article presents an overview of Guidance, Navigation and Control (GNC)
methodologies developed for space manipulators to perform in-orbit robotic missions …
methodologies developed for space manipulators to perform in-orbit robotic missions …
Data-driven model predictive control using random forests for building energy optimization and climate control
Abstract Model Predictive Control (MPC) is a model-based technique widely and
successfully used over the past years to improve control systems performance. A key factor …
successfully used over the past years to improve control systems performance. A key factor …