1 citations · 2 across the 6 of their papers we have counts for
6 papers
Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination
Usman Haider, Karl Mason
Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not…
Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
Mian Ibad Ali Shah, Marcos Eduardo Cruz Victorio, Maeve Duffy +2
The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading.…
Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning
Mian Ibad Ali Shah, Enda Barrett, Karl Mason
This paper presents a novel framework for Peer-to-Peer (P2P) energy trading that integrates uncertainty-aware prediction with multi-agent reinforcement learning (MARL), addressing…
Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms
Mian Ibad Ali Shah, Enda Barrett, Karl Mason
Farm businesses are increasingly adopting renewables to enhance energy efficiency and reduce reliance on fossil fuels and the grid. This shift aims to decrease dairy farms' depende…
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
Nawazish Ali, Abdul Wahid, Rachael Shaw +1
Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could hel…
A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming
Mian Ibad Ali Shah, Abdul Wahid, Enda Barrett +1
To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially importan…