[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 …

[HTML][HTML] Synergies and potential of hybrid solar photovoltaic-thermal desalination technologies

W He, G Huang, CN Markides - Desalination, 2023 - Elsevier
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 …

[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 …

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 …

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 …

Machine learning toward advanced energy storage devices and systems

T Gao, W Lu - Iscience, 2021 - cell.com
Technology advancement demands energy storage devices (ESD) and systems (ESS) with
better performance, longer life, higher reliability, and smarter management strategy …

Review of energy storage and energy management system control strategies in microgrids

G Chaudhary, JJ Lamb, OS Burheim, B Austbø - Energies, 2021 - mdpi.com
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 …

Reinforcement learning techniques for optimal power control in grid-connected microgrids: A comprehensive review

EO Arwa, KA Folly - Ieee Access, 2020 - ieeexplore.ieee.org
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 …

Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids

P Kofinas, AI Dounis, GA Vouros - Applied energy, 2018 - Elsevier
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 …

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 …