[HTML][HTML] Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit
about one-third of greenhouse gases. In the last few years, machine learning has achieved …
about one-third of greenhouse gases. In the last few years, machine learning has achieved …
A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
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] Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control
Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level
X Fang, G Gong, G Li, L Chun, P Peng, W Li, X Shi - Energy, 2023 - Elsevier
Abstract Model free based DRL control strategies have achieved positive effects on the
HVAC system optimal control. However, developing deep reinforcement learning (DRL) …
HVAC system optimal control. However, developing deep reinforcement learning (DRL) …
Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings
In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL)
proved to be effective in optimizing the management of integrated energy systems in …
proved to be effective in optimizing the management of integrated energy systems in …
Reinforcement learning of occupant behavior model for cross-building transfer learning to various HVAC control systems
Occupant behavior plays an important role in the evaluation of building performance.
However, many contextual factors, such as occupancy, mechanical system and interior …
However, many contextual factors, such as occupancy, mechanical system and interior …
[HTML][HTML] Reinforcement learning building control approach harnessing imitation learning
Reinforcement learning (RL) has shown significant success in sequential decision making in
fields like autonomous vehicles, robotics, marketing and gaming industries. This success …
fields like autonomous vehicles, robotics, marketing and gaming industries. This success …
Data science for building energy efficiency: A comprehensive text-mining driven review of scientific literature
The ever-changing data science landscape is fueling innovation in the built environment
context by providing new and more effective means of converting large raw data sets into …
context by providing new and more effective means of converting large raw data sets into …