[HTML][HTML] Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review
Abstract Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are a major
source of global operational CO 2 emissions, primarily due to their high energy demands …
source of global operational CO 2 emissions, primarily due to their high energy demands …
[HTML][HTML] AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings
DMTE Ali, V Motuzienė, R Džiugaitė-Tumėnienė - Energies, 2024 - mdpi.com
Despite the tightening of energy performance standards for buildings in various countries
and the increased use of efficient and renewable energy technologies, it is clear that the …
and the increased use of efficient and renewable energy technologies, it is clear that the …
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 …
Towards self-learning control of HVAC systems with the consideration of dynamic occupancy patterns: Application of model-free deep reinforcement learning
M Esrafilian-Najafabadi, F Haghighat - Building and Environment, 2022 - Elsevier
This study proposes a self-learning control system that aims to learn occupancy profiles,
building energy consumption patterns, and lag-time of the heating, ventilation, and air …
building energy consumption patterns, and lag-time of the heating, ventilation, and air …
Energy-efficient control of indoor PM2. 5 and thermal comfort in a real room using deep reinforcement learning
Abstract To reduce indoor PM 2.5 (particulate matter with aerodynamic diameter less than
2.5 μm) pollution and maintain thermal comfort with relatively low energy consumption, this …
2.5 μm) pollution and maintain thermal comfort with relatively low energy consumption, this …
Prospects and challenges of reinforcement learning-based HVAC control
Increasing worldwide energy demand and the resulting escalations in greenhouse gas
emissions require a reassessment of energy usage in many sectors. The building industry …
emissions require a reassessment of energy usage in many sectors. The building industry …
Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems
This paper presents Model-based Reinforcement Learning (MB-RL) techniques to control
the indoor air temperature, and CO2 concentration level, and minimize the energy …
the indoor air temperature, and CO2 concentration level, and minimize the energy …
Systematic review on deep reinforcement learning-based energy management for different building types
A Shaqour, A Hagishima - Energies, 2022 - mdpi.com
Owing to the high energy demand of buildings, which accounted for 36% of the global share
in 2020, they are one of the core targets for energy-efficiency research and regulations …
in 2020, they are one of the core targets for energy-efficiency research and regulations …
[HTML][HTML] Using Pearson correlation coefficient as a performance indicator in the compensation algorithm of asynchronous temperature-humidity sensor pair
TP Teng, WJ Chen - Case Studies in Thermal Engineering, 2024 - Elsevier
Artificial Intelligence (AI) based control algorithms for heating, ventilation, and air
conditioning (HVAC) equipment have been gradually applied to improve building energy …
conditioning (HVAC) equipment have been gradually applied to improve building energy …
Development and evaluation of data-driven controls for residential smart thermostats
The advent of smart thermostats with real-time sensing raises the question of how to
preemptively control heating, ventilation, and air conditioning (HVAC) systems to minimize …
preemptively control heating, ventilation, and air conditioning (HVAC) systems to minimize …