[HTML][HTML] Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value
This paper presents an Energy Management (EM) strategy for residential microgrid systems
using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley …
using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley …
CNN-LSTM Based Deep Learning Application on Jetson Nano: Estimating Electrical Energy Consumption for Future Smart Homes
Smart home applications have witnessed significant advancements, expanding beyond
lighting control or remote monitoring to more sophisticated functionalities. Our study delves …
lighting control or remote monitoring to more sophisticated functionalities. Our study delves …
Mutual interference-aware throughput enhancement in massive IoT: A graph reinforcement learning framework
F Yang, C Yang, J Huang, O Alfarraj… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
As the number of devices increases dramatically in the Internet of Things (IoT), features of
dense deployment of massive devices generate mutual interference in communication …
dense deployment of massive devices generate mutual interference in communication …
A novel hybrid-action-based deep reinforcement learning for industrial energy management
R Lu, Z Jiang, T Yang, Y Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
As environmental pollution becomes increasingly serious and industrial energy
consumption continuously rises, an intelligent and efficient industrial energy management …
consumption continuously rises, an intelligent and efficient industrial energy management …
Deep reinforcement learning with reward shaping for tracking control and vibration suppression of flexible link manipulator
JK Viswanadhapalli, VK Elumalai, S Shivram… - Applied Soft …, 2024 - Elsevier
This paper puts forward a novel deep reinforcement learning control using deep
deterministic policy gradient (DRLC-DDPG) framework to address the reference tracking …
deterministic policy gradient (DRLC-DDPG) framework to address the reference tracking …
Multi-objective energy management of a smart home in real time environment
In home energy management, the occupants schedule the operating appliances to achieve
lowest optimal energy cost with minimum discomfort. Smart home energy management turns …
lowest optimal energy cost with minimum discomfort. Smart home energy management turns …
Reward adaptive wind power tracking control based on deep deterministic policy gradient
P Chen, D Han - Applied Energy, 2023 - Elsevier
Wind power efficiency is an essential factor affecting wind power development, and efficient
wind power control methods are the key to improving wind power efficiency. Previous wind …
wind power control methods are the key to improving wind power efficiency. Previous wind …
A home energy management approach using decoupling value and policy in reinforcement learning
Considering the popularity of electric vehicles and the flexibility of household appliances, it
is feasible to dispatch energy in home energy systems under dynamic electricity prices to …
is feasible to dispatch energy in home energy systems under dynamic electricity prices to …
Blockchain-driven real-time incentive approach for energy management system
In the current era, the skyrocketing demand for energy necessitates a powerful mechanism
to mitigate the supply–demand gap in intelligent energy infrastructure, ie, the smart grid. To …
to mitigate the supply–demand gap in intelligent energy infrastructure, ie, the smart grid. To …
Adaptive Optimal Surrounding Control of Multiple Unmanned Surface Vessels via Actor-Critic Reinforcement Learning
R Lu, X Wang, Y Ding, HT Zhang… - … on Neural Networks …, 2024 - ieeexplore.ieee.org
In this article, an optimal surrounding control algorithm is proposed for multiple unmanned
surface vessels (USVs), in which actor-critic reinforcement learning (RL) is utilized to …
surface vessels (USVs), in which actor-critic reinforcement learning (RL) is utilized to …