Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

Yoon, Yeobeom; Amasyali, Kadir; Li, Yanfei; Im, Piljae; Bae, Yeonjin; Liu, Yan; Zandi, Helia

doi:10.1016/j.enbuild.2024.115005

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

Journal Article · Thu Nov 07 00:00:00 EST 2024 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2024.115005· OSTI ID:2478346

^[1]; Amasyali, Kadir ^[2]; ^[1]; ^[1]; ^[1]; ^[1]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Carrier HVAC (United States)

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2478346

Journal Information:: Energy and Buildings, Journal Name: Energy and Buildings Vol. 325; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (21)

Sensor impacts on building and HVAC controls: A critical review for building energy performance Bae, Yeonjin; Bhattacharya, Saptarshi; Cui, Borui Advances in Applied Energy, Vol. 4 https://doi.org/10.1016/j.adapen.2021.100068	journal	November 2021
Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building Alimohammadisagvand, Behrang; Jokisalo, Juha; Sirén, Kai Applied Energy, Vol. 209 https://doi.org/10.1016/j.apenergy.2017.10.088	journal	January 2018
Development of grid-responsive buildings: Opportunities, challenges, capabilities and applications of HVAC systems in non-residential buildings in providing ancillary services by fast demand responses to smart grids Wang, Huilong; Wang, Shengwei; Tang, Rui Applied Energy, Vol. 250 https://doi.org/10.1016/j.apenergy.2019.04.159	journal	September 2019
Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season Im, Piljae; Joe, Jaewan; Bae, Yeonjin Applied Energy, Vol. 261 https://doi.org/10.1016/j.apenergy.2019.114374	journal	March 2020
Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates Pang, Zhihong; Chen, Yan; Zhang, Jian Applied Energy, Vol. 279 https://doi.org/10.1016/j.apenergy.2020.115727	journal	December 2020
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning Du, Yan; Zandi, Helia; Kotevska, Olivera Applied Energy, Vol. 281 https://doi.org/10.1016/j.apenergy.2020.116117	journal	January 2021
Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control Biemann, Marco; Scheller, Fabian; Liu, Xiufeng Applied Energy, Vol. 298 https://doi.org/10.1016/j.apenergy.2021.117164	journal	September 2021
Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach Wang, Hao; Chen, Xiwen; Vital, Natan Applied Energy, Vol. 356 https://doi.org/10.1016/j.apenergy.2023.122354	journal	February 2024
Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings Hoyt, Tyler; Arens, Edward; Zhang, Hui Building and Environment, Vol. 88 https://doi.org/10.1016/j.buildenv.2014.09.010	journal	June 2015
Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability Faulkner, Cary A.; Lutes, Robert; Huang, Sen Building and Environment, Vol. 240 https://doi.org/10.1016/j.buildenv.2023.110371	journal	July 2023
Ten questions concerning reinforcement learning for building energy management Nagy, Zoltan; Henze, Gregor; Dey, Sourav Building and Environment, Vol. 241 https://doi.org/10.1016/j.buildenv.2023.110435	journal	August 2023
A review on buildings energy consumption information Pérez-Lombard, Luis; Ortiz, José; Pout, Christine Energy and Buildings, Vol. 40, Issue 3 https://doi.org/10.1016/j.enbuild.2007.03.007	journal	January 2008
Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling Yoon, Young Ran; Moon, Hyeun Jun Energy and Buildings, Vol. 203 https://doi.org/10.1016/j.enbuild.2019.109420	journal	November 2019
Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system Schreiber, Thomas; Eschweiler, Sören; Baranski, Marc Energy and Buildings, Vol. 229 https://doi.org/10.1016/j.enbuild.2020.110490	journal	December 2020
Evaluation of advanced control strategies for building energy systems Stoffel, Phillip; Maier, Laura; Kümpel, Alexander Energy and Buildings, Vol. 280 https://doi.org/10.1016/j.enbuild.2022.112709	journal	February 2023
Benchmarking high performance HVAC Rule-Based controls with advanced intelligent Controllers: A case study in a Multi-Zone system in Modelica Lu, Xing; Fu, Yangyang; O'Neill, Zheng Energy and Buildings, Vol. 284 https://doi.org/10.1016/j.enbuild.2023.112854	journal	April 2023
Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season Yoon, Y.; Jung, S.; Im, P. Renewable and Sustainable Energy Reviews, Vol. 188 https://doi.org/10.1016/j.rser.2023.113889	journal	December 2023
Supervisory and Optimal Control of Building HVAC Systems: A Review Wang, Shengwei; Ma, Zhenjun HVAC&R Research, Vol. 14, Issue 1 https://doi.org/10.1080/10789669.2008.10390991	journal	January 2008
Estimating ASHRAE Guideline 36 energy savings for multi-zone variable air volume systems using Spawn of EnergyPlus Zhang, Kun; Blum, David; Cheng, Hwakong Journal of Building Performance Simulation, Vol. 15, Issue 2 https://doi.org/10.1080/19401493.2021.2021286	journal	January 2022
A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings Amasyali, Kadir; Liu, Yan; Zandi, Helia 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) https://doi.org/10.1109/ISGT59692.2024.10454120	conference	February 2024
Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends Kim, Dongsu; Lee, Jongman; Do, Sunglok Energies, Vol. 15, Issue 19 https://doi.org/10.3390/en15197231	journal	October 2022

Similar Records

Implementation of ASHRAE Guideline 36 Control Logic into Oak Ridge National Laboratory (ORNL)’s Flexible Research Platform (FRP)

Technical Report · Fri Apr 01 00:00:00 EDT 2022 · OSTI ID:1871093

Energy Saving Estimation of ASHRAE Guideline 36 Supervisory Setpoint Reset Controls in a Commercial Large Office Building

Conference · Tue Jun 25 00:00:00 EDT 2024 · OSTI ID:2566063

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
ASHRAE Guideline 36
Energy consumption
Field demonstration
Reinforcement learning–based control
Rooftop unit
Variable air volume system

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

Citation Formats

References (21)

Similar Records

Related Subjects