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 …
[HTML][HTML] A review of reinforcement learning for controlling building energy systems from a computer science perspective
D Weinberg, Q Wang, TO Timoudas… - Sustainable cities and …, 2023 - Elsevier
Energy efficient control of energy systems in buildings is a widely recognized challenge due
to the use of low temperature heating, renewable electricity sources, and the incorporation of …
to the use of low temperature heating, renewable electricity sources, and the incorporation of …
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 …
Review and evaluation of reinforcement learning frameworks on smart grid applications
With the rise in electricity, gas and oil prices and the persistently high levels of carbon
emissions, there is an increasing demand for effective energy management in energy …
emissions, there is an increasing demand for effective energy management in energy …
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 …
CityLearn v1. 0: An OpenAI gym environment for demand response with deep reinforcement learning
Demand response has the potential of reducing peaks of electricity demand by about 20% in
the US, where buildings represent roughly 70% of the total electricity demand. Buildings are …
the US, where buildings represent roughly 70% of the total electricity demand. Buildings are …
[HTML][HTML] Optimization of building demand flexibility using reinforcement learning and rule-based expert systems
The increasing use of renewable energy in buildings requires optimization of building
demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the …
demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the …
Enforcing policy feasibility constraints through differentiable projection for energy optimization
While reinforcement learning (RL) is gaining popularity in energy systems control, its real-
world applications are limited due to the fact that the actions from learned policies may not …
world applications are limited due to the fact that the actions from learned policies may not …
Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system
T Schreiber, S Eschweiler, M Baranski, D Müller - Energy and Buildings, 2020 - Elsevier
With the increasing use of volatile renewable energies, the requirements for building
automation and control systems (BACS) are increasing. Load shifting within local energy …
automation and control systems (BACS) are increasing. Load shifting within local energy …
CityLearn: Standardizing research in multi-agent reinforcement learning for demand response and urban energy management
Rapid urbanization, increasing integration of distributed renewable energy resources,
energy storage, and electric vehicles introduce new challenges for the power grid. In the US …
energy storage, and electric vehicles introduce new challenges for the power grid. In the US …