[HTML][HTML] An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are
challenged by unpredicted changes in operational environments due to climate change and …
challenged by unpredicted changes in operational environments due to climate change and …
[HTML][HTML] Applications of reinforcement learning in energy systems
ATD Perera, P Kamalaruban - Renewable and Sustainable Energy …, 2021 - Elsevier
Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …
renewable energy technologies and improve efficiencies, leading to the integration of many …
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 …
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 …
[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 …
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 …
Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy
R Shen, S Zhong, X Wen, Q An, R Zheng, Y Li, J Zhao - Applied Energy, 2022 - Elsevier
Under the background of high global building energy consumption, meeting the ever-
growing energy consumption demand of building energy system (BES) through renewable …
growing energy consumption demand of building energy system (BES) through renewable …
Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach
Indoor environmental quality is an important issue since people spend most of their time
indoors. This paper aims to develop an autonomous indoor environment management …
indoors. This paper aims to develop an autonomous indoor environment management …