Deep reinforcement learning for building HVAC control

By Tianshu Wei, Yanzhi Wang, Qi Zhu. Design Automation Conference, 2017

516 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/163a35665374fcd5283da47a7ee611af7df2d876

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Na Li
2. Steven H. Low
3. Yujie Tang
4. X. Guan
5. Tao Jiang
6. Xin Chen
7. Guannan Qu
8. Yulong Zou

In Renewable Energy, Sustainability and the Environment

1. Tao Jiang

Citations in the corpus

• A Review of Deep Reinforcement Learning for Smart Building Energy Management (2020, methodology): “[19] Office HVAC Energy cost Thermal comfort DQN, DNN 19.”
• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges (2021, background): “To this end, a number of studies [128]–[132] leverage DRL for HVAC system control.”
• Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control (2019, background): “Neural Networks for Dynamics and Control: Inferring underlying dynamics from time-series data plays a critical role in controlling closed-loop response of dynamical systems, such as robotic manipulators (Lillicrap et al., 2015) and building HVAC systems (Wei et al., 2017).”
• Deep Reinforcement Learning for Smart Home Energy Management (2019, methodology): “1) Baseline1: This scheme adopts ON/OFF policy [3] for building HVAC control but without considering the use of the ESS.”