Heating, Ventilation, and Air Conditioning

Heating, ventilation, and air conditioning (HVAC) systems are crucial for building comfort and significantly impact energy consumption and emissions. Current research focuses on optimizing HVAC control using reinforcement learning (RL) algorithms, particularly deep RL and model-based RL approaches, to improve energy efficiency and reduce environmental impact. These methods leverage both real-world data and simulations, often employing techniques like federated learning to enhance generalization and reduce training time. The development of automated modeling systems and data-driven approaches, including symbolic regression and unsupervised learning for fault detection, are also key areas of advancement, promising significant improvements in building energy management and sustainability.

Papers

October 2, 2024

Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability
Judah Goldfeder, John Sipple
Reinforcement Learning Reinforcement Learning Agent Energy Policy Research Real World Data Model Calibration Smart Building Heating, Ventilation, and Air Conditioning Offline Training

August 15, 2024

Experimental evaluation of offline reinforcement learning for HVAC control in buildings
Jun Wang, Linyan Li, Qi Liu, Yu Yang
Reinforcement Learning Offline Reinforcement Learning Residential Building Heating, Ventilation, and Air Conditioning

July 2, 2024

AHMsys: An Automated HVAC Modeling System for BIM Project
Long Hoang Dang, Duy-Hung Nguyen, Thai Quang Le, Thinh Truong Nguyen, Clark Mei, Vu Hoang
3D Object Heating, Ventilation, and Air Conditioning Building Information Building Information Modeling Temperature Evolution

June 6, 2024

HORAE: A Domain-Agnostic Modeling Language for Automating Multimodal Service Regulation
Yutao Sun, Mingshuai Chen, Tiancheng Zhao, Kangjia Zhao, He Li, Jintao Chen, Liqiang Lu, Xinkui Zhao, Shuiguang Deng, Jianwei Yin
Unified Model Domain Agnostic Heating, Ventilation, and Air Conditioning Multimodal Policy

May 1, 2024

Employing Federated Learning for Training Autonomous HVAC Systems
Fredrik Hagström, Vikas Garg, Fabricio Oliveira
Reinforcement Learning Federated Optimization Heating, Ventilation, and Air Conditioning Global Control

February 29, 2024

Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
Zhiyu An, Xianzhong Ding, Wan Du
Product Design Model Based Reinforcement Learning Black Box Model Interpretable Way Learning Agent Policy Adaptation Heating, Ventilation, and Air Conditioning

February 21, 2024

Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering
Dafang Zhao, Zheng Chen, Zhengmao Li, Xiaolei Yuan, Ittetsu Taniguchi
K Mean Data Driven Approach Energy Management Heating, Ventilation, and Air Conditioning Temperature Forecasting

January 11, 2024

An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
Antonio Manjavacas, Alejandro Campoy-Nieves, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero
Deep Reinforcement Learning Heating, Ventilation, and Air Conditioning Reactive Control

December 18, 2023

Unsupervised Learning for Fault Detection of HVAC Systems: An OPTICS -based Approach for Terminal Air Handling Units
Farivar Rajabi, J. J. McArthur
Unsupervised Learning Principal Component Analysis Fault Detection PCa Detection Cluster Analysis Smart Building Heating, Ventilation, and Air Conditioning Data Driven Fault Terminal Airspace

September 16, 2023

Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls
Zeynep Duygu Tekler, Yue Lei, Xilei Dai, Adrian Chong
Active Learning Heating, Ventilation, and Air Conditioning Thermal Comfort Occupancy Detection Thermal Preference

September 13, 2023

Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems
Daniel Bayer, Marco Pruckner
Reinforcement Learning Multi Agent Reinforcement Learning System Performance Reinforcement Learning Agent Heating, Ventilation, and Air Conditioning

August 10, 2023

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
Marshall Wang, John Willes, Thomas Jiralerspong, Matin Moezzi
Reinforcement Learning Consistent Comparison Q Learning Heating, Ventilation, and Air Conditioning

June 29, 2023

Laxity-Aware Scalable Reinforcement Learning for HVAC Control
Ruohong Liu, Yuxin Pan, Yize Chen
Reinforcement Learning Energy Efficiency Heating, Ventilation, and Air Conditioning Ditching Load

June 23, 2023

Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach
Hao Wang, Xiwen Chen, Natan Vital, Edward. Duffy, Abolfazl Razi
Energy Consumption Energy Efficiency Power Consumption Deep Reinforcement Learning Approach Building Energy Heating, Ventilation, and Air Conditioning Open Plan Office

April 6, 2023

Data-driven HVAC Control Using Symbolic Regression: Design and Implementation
Yuki Ozawa, Dafang Zhao, Daichi Watari, Ittetsu Taniguchi, Toshihiro Suzuki, Yoshiyuki Shimoda, Takao Onoye
Product Design Predictive Control Practical Implementation Symbolic Regression Heating, Ventilation, and Air Conditioning

March 3, 2023

Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II
Kanisius Karyono, Badr M. Abdullah, Alison J. Cotgrave, Ana Bras, Jeff Cullen
Training Data Shallow Learning Heating, Ventilation, and Air Conditioning Thermal Comfort

September 16, 2022

Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning
William Wong, Praneet Dutta, Octavian Voicu, Yuri Chervonyi, Cosmin Paduraru, Jerry Luo
Reinforcement Learning Hierarchical Reinforcement Learning Reward Engineering Heating, Ventilation, and Air Conditioning Industrial Cooling System

August 12, 2022

Low Emission Building Control with Zero-Shot Reinforcement Learning
Scott R. Jeen, Alessandro Abate, Jonathan M. Cullen
Reinforcement Learning Zero Shot Model Based Building Energy Heating, Ventilation, and Air Conditioning

June 28, 2022

Low Emission Building Control with Zero-Shot Reinforcement Learning
Scott R. Jeen, Alessandro Abate, Jonathan M. Cullen
Reinforcement Learning Zero Shot Model Based Building Energy Heating, Ventilation, and Air Conditioning

Heating, Ventilation, and Air Conditioning

Papers

Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability

Experimental evaluation of offline reinforcement learning for HVAC control in buildings

AHMsys: An Automated HVAC Modeling System for BIM Project

HORAE: A Domain-Agnostic Modeling Language for Automating Multimodal Service Regulation

Employing Federated Learning for Training Autonomous HVAC Systems

Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control

Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering

An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

Unsupervised Learning for Fault Detection of HVAC Systems: An OPTICS -based Approach for Terminal Air Handling Units

Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls

Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems

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

Laxity-Aware Scalable Reinforcement Learning for HVAC Control

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

Data-driven HVAC Control Using Symbolic Regression: Design and Implementation

Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II

Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning

Low Emission Building Control with Zero-Shot Reinforcement Learning

Low Emission Building Control with Zero-Shot Reinforcement Learning