Ten questions concerning reinforcement learning for building energy management
As buildings account for approximately 40% of global energy consumption and associated
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …
[HTML][HTML] Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
Building upon prior research that highlighted the need for standardizing environments for
building control research, and inspired by recently introduced challenges for real life …
building control research, and inspired by recently introduced challenges for real life …
Reinforcement learning for sustainable energy: A survey
K Ponse, F Kleuker, M Fejér, Á Serra-Gómez… - arXiv preprint arXiv …, 2024 - arxiv.org
The transition to sustainable energy is a key challenge of our time, requiring modifications in
the entire pipeline of energy production, storage, transmission, and consumption. At every …
the entire pipeline of energy production, storage, transmission, and consumption. At every …
Mitigating an adoption barrier of reinforcement learning-based control strategies in buildings
Reinforcement learning (RL) algorithms have shown great promise in controlling building
systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn …
systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn …
Diversity for transfer in learning-based control of buildings
The application of reinforcement learning to the optimal control of building systems has
gained traction in recent years as it can cut the building energy consumption and improve …
gained traction in recent years as it can cut the building energy consumption and improve …
On the joint control of multiple building systems with reinforcement learning
Commercial buildings are comprised of multiple mechanical and electrical systems that work
in tandem to provide a healthy, safe, and comfortable environment for occupants. These …
in tandem to provide a healthy, safe, and comfortable environment for occupants. These …
Sinergym–A virtual testbed for building energy optimization with Reinforcement Learning
A Campoy-Nieves, A Manjavacas… - Energy and …, 2025 - Elsevier
Simulation has become a crucial tool for Building Energy Optimization (BEO) as it enables
the evaluation of different design and control strategies at a low cost. Machine Learning (ML) …
the evaluation of different design and control strategies at a low cost. Machine Learning (ML) …
The impact of forecast characteristics on the forecast value for the dispatchable feeder
Transforming the energy system to decentralised, renewable energy sources requires
measures to balance their fluctuating nature and stabilise the energy system. One such …
measures to balance their fluctuating nature and stabilise the energy system. One such …
Enhancing the performance of multi-agent reinforcement learning for controlling HVAC systems
D Bayer, M Pruckner - 2022 IEEE Conference on Technologies …, 2022 - ieeexplore.ieee.org
Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally
controlled by a rule-based approach. In order to reduce the energy consumption and the …
controlled by a rule-based approach. In order to reduce the energy consumption and the …
Beobench: a toolkit for unified access to building simulations for reinforcement learning
Reinforcement learning (RL) is often considered a promising approach for controlling
complex building operations. In this context, RL algorithms are typically evaluated using a …
complex building operations. In this context, RL algorithms are typically evaluated using a …