Reinforcement learning for building controls: The opportunities and challenges
Building controls are becoming more important and complicated due to the dynamic and
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
Application of machine learning in thermal comfort studies: A review of methods, performance and challenges
ZQ Fard, ZS Zomorodian, SS Korsavi - Energy and Buildings, 2022 - Elsevier
This paper provides a systematic review on the application of Machine Learning (ML) in
thermal comfort studies to highlight the latest methods and findings and provide an agenda …
thermal comfort studies to highlight the latest methods and findings and provide an agenda …
Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality
The indoor environment directly affects health and comfort as humans spend most of the day
indoors. However, improperly controlled ventilation systems can expend unnecessary …
indoors. However, improperly controlled ventilation systems can expend unnecessary …
Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm
L Xiong, Y Yao - Building and Environment, 2021 - Elsevier
Compared with the static thermal comfort models like predicted mean vote (PMV) model,
adaptive thermal models have a wider range of adaptability. The traditional concept of …
adaptive thermal models have a wider range of adaptability. The traditional concept of …
Applications of reinforcement learning for building energy efficiency control: A review
Q Fu, Z Han, J Chen, Y Lu, H Wu, Y Wang - Journal of Building Engineering, 2022 - Elsevier
The wide variety of smart devices equipped in modern intelligent buildings and the
increasing comfort requirements of occupants for the environment make the control of …
increasing comfort requirements of occupants for the environment make the control of …
Review on occupant-centric thermal comfort sensing, predicting, and controlling
Ensuring occupants' thermal comfort and work performance is one of the primary objectives
for building environment conditioning systems. In recent years, there emerged many …
for building environment conditioning systems. In recent years, there emerged many …
The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls
Buildings consume about 40% of globally-produced energy. A notable amount of this energy
is used to provide sufficient comfort levels to the building occupants. Moreover, given recent …
is used to provide sufficient comfort levels to the building occupants. Moreover, given recent …
Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II
Predicting building occupants' thermal comfort via machine learning (ML) is a hot research
topic. Many algorithms and data processing methods have been applied to predict thermal …
topic. Many algorithms and data processing methods have been applied to predict thermal …
A hybrid deep transfer learning strategy for thermal comfort prediction in buildings
Since the thermal condition of living spaces affects the occupants' productivity and their
quality of life, it is important to design effective heating, ventilation and air conditioning …
quality of life, it is important to design effective heating, ventilation and air conditioning …
A review of reinforcement learning methodologies for controlling occupant comfort in buildings
Classical building control systems are becoming vulnerable with increasing complexities in
contemporary built environments and energy systems. Due to this, the reinforcement …
contemporary built environments and energy systems. Due to this, the reinforcement …