[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] Synergies and potential of hybrid solar photovoltaic-thermal desalination technologies
Solar desalination has emerged as a sustainable solution for addressing global water
scarcity in the energy-water nexus, particularly for remote areas in developing countries …
scarcity in the energy-water nexus, particularly for remote areas in developing countries …
[HTML][HTML] Deep reinforcement learning for energy management in a microgrid with flexible demand
TA Nakabi, P Toivanen - Sustainable Energy, Grids and Networks, 2021 - Elsevier
In this paper, we study the performance of various deep reinforcement learning algorithms to
enhance the energy management system of a microgrid. We propose a novel microgrid …
enhance the energy management system of a microgrid. We propose a novel microgrid …
A dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
R Lu, SH Hong, X Zhang - Applied energy, 2018 - Elsevier
With the modern advanced information and communication technologies in smart grid
systems, demand response (DR) has become an effective method for improving grid …
systems, demand response (DR) has become an effective method for improving grid …
A review of reinforcement learning for autonomous building energy management
K Mason, S Grijalva - Computers & Electrical Engineering, 2019 - Elsevier
The area of building energy management has received a significant amount of interest in
recent years. This area is concerned with combining advancements in sensor technologies …
recent years. This area is concerned with combining advancements in sensor technologies …
Review of energy storage and energy management system control strategies in microgrids
A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed
energy sources and loads capable of operating in parallel with or independently from the …
energy sources and loads capable of operating in parallel with or independently from the …
Reinforcement learning techniques for optimal power control in grid-connected microgrids: A comprehensive review
Utility grids are undergoing several upgrades. Distributed generators that are supplied by
intermittent renewable energy sources (RES) are being connected to the grids. As RES get …
intermittent renewable energy sources (RES) are being connected to the grids. As RES get …
Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids
This study proposes a cooperative multi-agent system for managing the energy of a stand-
alone microgrid. The multi-agent system learns to control the components of the microgrid so …
alone microgrid. The multi-agent system learns to control the components of the microgrid so …
Feedback deep deterministic policy gradient with fuzzy reward for robotic multiple peg-in-hole assembly tasks
J Xu, Z Hou, W Wang, B Xu, K Zhang… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The automatic completion of multiple peg-in-hole assembly tasks by robots remains a
formidable challenge because the traditional control strategies require a complex analysis of …
formidable challenge because the traditional control strategies require a complex analysis of …