Reinforcement learning applied to wastewater treatment process control optimization: Approaches, challenges, and path forward
Wastewater treatment process control optimization is a complex task in a highly nonlinear
environment. Reinforcement learning (RL) is a machine learning technique that stands out …
environment. Reinforcement learning (RL) is a machine learning technique that stands out …
Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm
W Valladares, M Galindo, J Gutiérrez, WC Wu… - Building and …, 2019 - Elsevier
The aim of this work is to propose an artificial intelligence algorithm that maintains thermal
comfort and air quality within optimal levels while consuming the least amount of energy …
comfort and air quality within optimal levels while consuming the least amount of energy …
Systematic performance evaluation of reinforcement learning algorithms applied to wastewater treatment control optimization
Treatment of wastewater using activated sludge relies on several complex, nonlinear
processes. While activated sludge systems can provide high levels of treatment, including …
processes. While activated sludge systems can provide high levels of treatment, including …
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 …
Residual physics and post-posed shielding for safe deep reinforcement learning method
Deep reinforcement learning (DRL) has been researched for computer room air conditioning
unit control problems in data centers (DCs). However, two main issues limit the deployment …
unit control problems in data centers (DCs). However, two main issues limit the deployment …
Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control
G Demirezen, AS Fung… - International Journal of …, 2020 - Wiley Online Library
This research accounts for the outcome of a major cloud‐based smart dual fuel switching
system (SDFSS) project, which is a dual‐fuel integrated hybrid heating, ventilation, and air …
system (SDFSS) project, which is a dual‐fuel integrated hybrid heating, ventilation, and air …
HVAC system modeling and control methods: a review and case study
Improvement of air quality and provision of the residents' comfort in different buildings are
the main tasks of HVAC (heating, ventilating, and air conditioning) systems. A large number …
the main tasks of HVAC (heating, ventilating, and air conditioning) systems. A large number …
Methodology for interpretable reinforcement learning model for HVAC energy control
Deep reinforcement learning (DRL) approaches have been used in various application
areas to improve efficiency, optimization, or automation. However, very little is known about …
areas to improve efficiency, optimization, or automation. However, very little is known about …
[PDF][PDF] Deep reinforcement learning on HVAC control
I Namatēvs - Information Technology and Management …, 2018 - pdfs.semanticscholar.org
Due to an increase in computing power and innovative approaches of an end-to-end
reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now …
reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now …
The potential of control models based on reinforcement learning in the operating of solar thermal cooling systems
JJ Diaz, JA Fernández - Processes, 2022 - mdpi.com
The objective of this research work was to investigate the potential of control models based
on reinforcement learning in the optimization of solar thermal cooling systems (STCS) …
on reinforcement learning in the optimization of solar thermal cooling systems (STCS) …