[HTML][HTML] Optimization of thermal comfort, indoor quality, and energy-saving in campus classroom through deep Q learning
KH Yu, YA Chen, E Jaimes, WC Wu, KK Liao… - Case Studies in Thermal …, 2021 - Elsevier
This study develops a control algorithm for optimization the energy consumptions of air-
conditioning and exhaust fans through Deep Q-Learning in reinforcement learning. The …
conditioning and exhaust fans through Deep Q-Learning in reinforcement learning. The …
MBRL-MC: An HVAC control approach via combining model-based deep reinforcement learning and model predictive control
This article proposes a novel learning-based control strategy, named MBRL-MC, for the
heating, ventilation, and air conditioning (HVAC) system by combining model-based deep …
heating, ventilation, and air conditioning (HVAC) system by combining model-based deep …
Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings
Advanced control strategies are central components of smart buildings. For model-based
control algorithms, the quality of the model that represents building systems and dynamics is …
control algorithms, the quality of the model that represents building systems and dynamics is …
[HTML][HTML] Design and implementation of an indoor environment management system using a deep reinforcement learning approach
A Alferidi, M Alsolami, B Lami, SB Slama - Ain Shams Engineering Journal, 2023 - Elsevier
Abstract The Indoor Household Environment (IHE) has caught the attention of industry and
academia due to the substantial amount of time that families spend indoors. Domestic air …
academia due to the substantial amount of time that families spend indoors. Domestic air …
How good are learning-based control vs model-based control for load shifting? Investigations on a single zone building energy system
Both model predictive control (MPC) and deep reinforcement learning control (DRL) have
been presented as a way to approximate the true optimality of a dynamic programming …
been presented as a way to approximate the true optimality of a dynamic programming …
Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
Whole building energy model (BEM) is a physics-based modeling method for building
energy simulation. It has been widely used in the building industry for code compliance …
energy simulation. It has been widely used in the building industry for code compliance …
Reinforcement learning of room temperature set-point of thermal storage air-conditioning system with demand response
Z Li, Z Sun, Q Meng, Y Wang, Y Li - Energy and Buildings, 2022 - Elsevier
Demand response (DR) is an effective means to reduce peak loads and enhance grid
stability. Heating, ventilation, and air-conditioning (HVAC) systems have potential energy …
stability. Heating, ventilation, and air-conditioning (HVAC) systems have potential energy …
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 …
Demystifying thermal comfort in smart buildings: An interpretable machine learning approach
Thermal comfort is a key consideration in smart buildings and a number of comfort models
are available nowadays to evaluate the comfort level of occupants. However, the models are …
are available nowadays to evaluate the comfort level of occupants. However, the models are …
Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency
Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic
(PV) technology and electric battery storage, are increasingly being considered as solutions …
(PV) technology and electric battery storage, are increasingly being considered as solutions …