Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques
GH Merabet, M Essaaidi, MB Haddou… - … and Sustainable Energy …, 2021 - Elsevier
Building operations represent a significant percentage of the total primary energy consumed
in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning …
in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning …
Deep reinforcement learning for power system applications: An overview
Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …
conventional methods often meet bottlenecks when attempting to solve decision and control …
Deep reinforcement learning for smart home energy management
We investigate an energy cost minimization problem for a smart home in the absence of a
building thermal dynamics model with the consideration of a comfortable temperature range …
building thermal dynamics model with the consideration of a comfortable temperature range …
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 …
A review of reinforcement learning for autonomous building energy management
K Mason, S Grijalva - Computers & Electrical Engineering, 2019 - Elsevier
The area of building energy management has received a significant amount of interest in
recent years. This area is concerned with combining advancements in sensor technologies …
recent years. This area is concerned with combining advancements in sensor technologies …
Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems
Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal
operation of building energy systems in the past two decades. However, MPC field tests are …
operation of building energy systems in the past two decades. However, MPC field tests are …
Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
Abstract In this work, Deep Reinforcement Learning (DRL) is implemented to control the
supply water temperature setpoint to terminal units of a heating system. The experiment was …
supply water temperature setpoint to terminal units of a heating system. The experiment was …
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 …
Optimization strategy based on deep reinforcement learning for home energy management
Y Liu, D Zhang, HB Gooi - CSEE Journal of Power and Energy …, 2020 - ieeexplore.ieee.org
With the development of a smart grid and smart home, massive amounts of data can be
made available, providing the basis for algorithm training in artificial intelligence …
made available, providing the basis for algorithm training in artificial intelligence …
A review of reinforcement learning methodologies for controlling occupant comfort in buildings
Classical building control systems are becoming vulnerable with increasing complexities in
contemporary built environments and energy systems. Due to this, the reinforcement …
contemporary built environments and energy systems. Due to this, the reinforcement …