Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

@article{Yu2021MultiAgentDR,
  title={Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings},
  author={Liang Yu and Yi Sun and Zhanbo Xu and Chao Shen and Dong Yue and Tao Jiang and Xiaohong Guan},
  journal={IEEE Transactions on Smart Grid},
  year={2021},
  volume={12},
  pages={407-419}
}
In commercial buildings, about 40%–50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g… 

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References

SHOWING 1-10 OF 54 REFERENCES
A. and Q
Energy Optimization of HVAC Systems in Commercial Buildings Considering Indoor Air Quality Management
TLDR
A real-time algorithm based on the framework of Lyapunov optimization techniques is proposed to construct virtual queues related to indoor temperatures and stabilize such queues so that indoor temperatures fluctuate around the ideal time-average indoor temperature.
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
TLDR
This work presents an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep, which enables more effective and scalable learning in complex multi- agent environments, when compared to recent approaches.
Transactive Control of Commercial Buildings for Demand Response
Transactive control is a type of distributed control strategy that uses market mechanisms to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this
and M
  • Berges, “Gnu-RL: A precocial reinforcement learning solution for building HVAC control using a differentiable MPC policy,” in Proc. BuildSys
  • 2019
and K
  • Tomsovic, “Community microgrid scheduling considering building thermal dynamics,” in Proc. IEEE Power Energy Soc. Gen. Meeting
  • 2017
Distributed Real-Time HVAC Control for Cost-Efficient Commercial Buildings Under Smart Grid Environment
TLDR
A real-time HVAC control algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters and know their stochastic information is proposed for minimizing the long-term total cost.
A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning
TLDR
A multi-agent AVC (MA-AVC) algorithm based on a multi- agent deep deterministic policy gradient (MADDPG) method that features centralized training and decentralized execution is developed to solve the AVC problem.
Learning-Automata-Based Confident Information Coverage Barriers for Smart Ocean Internet of Things
TLDR
A novel and widely adopted confident information coverage model is adopted as the fundamental coverage model and the CIC barrier path construction (CICBC) problem is formulated with the goals of maximizing the number of barrier paths and minimizing the amount of IoT nodes in each barrier path.
Real-Time Residential Demand Response
TLDR
In the proposed approach, an approximate optimal policy based on neural network is designed to learn the optimal DR scheduling strategy and can directly learn from high-dimensional sensory data of the appliance states, real-time electricity price, and outdoor temperature.
...
...