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 …
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 …
A review of reinforcement learning applications to control of heating, ventilation and air conditioning systems
Reinforcement learning has emerged as a potentially disruptive technology for control and
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …
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 …
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 …
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 …
[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 …
Deep reinforcement learning for autonomous water heater control
Electric water heaters represent 14% of the electricity consumption in residential buildings.
An average household in the United States (US) spends about USD 400–600 (0.45¢/L …
An average household in the United States (US) spends about USD 400–600 (0.45¢/L …
Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning
ZK Ding, QM Fu, JP Chen, HJ Wu, Y Lu… - Connection Science, 2022 - Taylor & Francis
Energy efficient control of thermal comfort has been already an important part of residential
heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of …
heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of …