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[HTML][HTML] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review
Implementing an efficient control strategy for heating, ventilation, and air conditioning
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning
Z Wang, F **ao, Y Ran, Y Li, Y Xu - Applied Energy, 2024 - Elsevier
Deploying renewable energy and implementing smart energy management strategies are
crucial for decarbonizing Building Energy Systems (BES). Despite recent advancements in …
crucial for decarbonizing Building Energy Systems (BES). Despite recent advancements in …
[HTML][HTML] Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control
Abstract Deep Reinforcement Learning (DRL) has emerged as a promising approach to
address the trade-off between energy efficiency and indoor comfort in buildings, potentially …
address the trade-off between energy efficiency and indoor comfort in buildings, potentially …
Process-oriented guidelines for systematic improvement of supervised learning research in construction engineering
A limited assessment of the development process and various stages of machine learning
(ML) based solutions for construction engineering (CE) problems are available in the …
(ML) based solutions for construction engineering (CE) problems are available in the …
Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures
Sufficient building operational data serve as the key premise to enable the development of
reliable data-driven technologies for building energy management. Considering that …
reliable data-driven technologies for building energy management. Considering that …
Field demonstration of predictive heating control for an all-electric house in a cold climate
Efficient electric heat pumps that replace fossil-fueled heating systems could significantly
reduce greenhouse gas emissions. However, electric heat pumps can sharply increase …
reduce greenhouse gas emissions. However, electric heat pumps can sharply increase …
Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building
With the recent demand for decarbonization and energy efficiency, advanced HVAC control
using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible …
using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible …
An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy …
Abstract Deep Reinforcement Learning (DRL)-based control shows enhanced performance
in the management of integrated energy systems when compared with Rule-Based …
in the management of integrated energy systems when compared with Rule-Based …
Expert-guided imitation learning for energy management: Evaluating GAIL's performance in building control applications
Abstract The use of Deep Reinforcement Learning (DRL) in building energy management is
often hampered by data efficiency and computational challenges. The long training time …
often hampered by data efficiency and computational challenges. The long training time …
Living laboratories can and should play a greater role to unlock flexibility in United States commercial buildings
Energy demand flexibility from commercial buildings can play a critical role in the ongoing
energy transition. There is an urgent need to redirect more research and deployment efforts …
energy transition. There is an urgent need to redirect more research and deployment efforts …