Reinforcement learning for building controls: The opportunities and challenges

Z Wang, T Hong - Applied Energy, 2020 - Elsevier
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 …

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 …

Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality

N Ma, D Aviv, H Guo, WW Braham - Renewable and Sustainable Energy …, 2021 - Elsevier
The indoor environment directly affects health and comfort as humans spend most of the day
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 …

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 …

Review on occupant-centric thermal comfort sensing, predicting, and controlling

J Xie, H Li, C Li, J Zhang, M Luo - Energy and Buildings, 2020 - Elsevier
Ensuring occupants' thermal comfort and work performance is one of the primary objectives
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

J Ngarambe, GY Yun, M Santamouris - Energy and Buildings, 2020 - Elsevier
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 …

Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II

M Luo, J Xie, Y Yan, Z Ke, P Yu, Z Wang, J Zhang - Energy and Buildings, 2020 - Elsevier
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 …

A hybrid deep transfer learning strategy for thermal comfort prediction in buildings

N Somu, A Sriram, A Kowli, K Ramamritham - Building and Environment, 2021 - Elsevier
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 …

A review of reinforcement learning methodologies for controlling occupant comfort in buildings

M Han, R May, X Zhang, X Wang, S Pan, D Yan… - Sustainable cities and …, 2019 - Elsevier
Classical building control systems are becoming vulnerable with increasing complexities in
contemporary built environments and energy systems. Due to this, the reinforcement …