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Reinforcement learning for building controls: The opportunities and challenges
Building controls are becoming more important and complicated due to the dynamic and
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
[HTML][HTML] Data-driven predictive control for unlocking building energy flexibility: A review
Managing supply and demand in the electricity grid is becoming more challenging due to
the increasing penetration of variable renewable energy sources. As significant end-use …
the increasing penetration of variable renewable energy sources. As significant end-use …
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 …
Review on the research and practice of deep learning and reinforcement learning in smart grids
D Zhang, X Han, C Deng - CSEE Journal of Power and Energy …, 2018 - ieeexplore.ieee.org
Smart grids are the developmental trend of power systems and they have attracted much
attention all over the world. Due to their complexities, and the uncertainty of the smart grid …
attention all over the world. Due to their complexities, and the uncertainty of the smart grid …
[HTML][HTML] Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control
Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
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 …
Deep reinforcement learning for building HVAC control
Buildings account for nearly 40% of the total energy consumption in the United States, about
half of which is used by the HVAC (heating, ventilation, and air conditioning) system …
half of which is used by the HVAC (heating, ventilation, and air conditioning) system …
Reinforcement learning for demand response: A review of algorithms and modeling techniques
JR Vázquez-Canteli, Z Nagy - Applied energy, 2019 - Elsevier
Buildings account for about 40% of the global energy consumption. Renewable energy
resources are one possibility to mitigate the dependence of residential buildings on the …
resources are one possibility to mitigate the dependence of residential buildings on the …
[HTML][HTML] Reinforcement learning for whole-building HVAC control and demand response
This paper proposes a novel reinforcement learning (RL) architecture for the efficient
scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a …
scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a …
[HTML][HTML] A comprehensive review of the applications of machine learning for HVAC
Heating, ventilation and air-conditioning (HVAC) accounts for around 40% of the total
building energy consumption. It has therefore become a major target for reductions, in terms …
building energy consumption. It has therefore become a major target for reductions, in terms …