Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning

2022-09-16 · William Wong, Praneet Dutta, Octavian Voicu, Yuri Chervonyi, Cosmin Paduraru, Jerry Luo

Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A major challenge in industrial control involves learning behaviors that are feasible in the real world due to machinery constraints. For example, certain actions can only be executed every few hours while other actions can be taken more frequently. Without extensive reward engineering and experimentation, an RL agent may not learn realistic operation of machinery. To address this, we use hierarchical reinforcement learning with multiple agents that control subsets of actions according to their operation time scales. Our hierarchical approach achieves energy savings over existing baselines while maintaining constraints such as operating chillers within safe bounds in a simulated HVAC control environment.

📄 PDF Abstract BibTeX arXiv:2209.08112

Code (0)

등록된 구현이 없습니다.

Tasks

Hierarchical Reinforcement Learning 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

A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation

2026-05-23 · Erfan Haghighat Damavandi, Davide Papurello, Mahdi Alibeigi, Armin Keshavarz 외 arxiv

Optimizing HVAC (Heating, Ventilation and Air Conditioning) can enhance a building's energy efficiency while providing comfort levels for its occupants. Using conventional control systems to maintain HVAC functions is of…

Reinforcement Learning

ReeM: Ensemble Building Thermodynamics Model for Efficient HVAC Control via Hierarchical Reinforcement Learning

2025-05-05 · Yang Deng, Yaohui Liu, Rui Liang, Dafang Zhao 외

The building thermodynamics model, which predicts real-time indoor temperature changes under potential HVAC (Heating, Ventilation, and Air Conditioning) control operations, is crucial for optimizing HVAC control in build…

Decision Making Hierarchical Reinforcement Learning Model Selection

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)

Laxity-Aware Scalable Reinforcement Learning for HVAC Control

2023-06-29 · Ruohong Liu, Yuxin Pan, Yize Chen

Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills. Given their highly shiftable load and significant contribution to a building's energy consumpti…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)