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

2020-06-25 · Liang Yu, Yi Sun, Zhanbo Xu, Chao Shen, Dong Yue, Tao Jiang, Xiaohong Guan

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 under dynamic pricing 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., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm.

📄 PDF Abstract BibTeX arXiv:2006.14156

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning (RL)

Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control

2021-10-26 · Vinay Hanumaiah, Sahika Genc

It is estimated that about 40%-50% of total electricity consumption in commercial buildings can be attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems. Minimizing the energy cost while considering the…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

2023-08-10 · Marshall Wang, John Willes, Thomas Jiralerspong, Matin Moezzi

Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popula…

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+1

An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

2024-01-11 · Antonio Manjavacas, Alejandro Campoy-Nieves, Javier Jiménez-Raboso, Miguel Molina-Solana 외

Heating, Ventilation, and Air Conditioning (HVAC) systems are a major driver of energy consumption in commercial and residential buildings. Recent studies have shown that Deep Reinforcement Learning (DRL) algorithms can …

Deep Reinforcement Learning Incremental Learning reinforcement-learning

Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach

2023-06-23 · Hao Wang, Xiwen Chen, Natan Vital, Edward. Duffy 외

With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). With HVAC systems accounting for about …

Deep Reinforcement Learning energy management

HVAC-DPT: A Decision Pretrained Transformer for HVAC Control

2024-11-29 · Anaïs Berkes

Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption. As HVAC energy demands are expected to rise, o…

In-Context Reinforcement Learning Reinforcement Learning (RL)