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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗
DOE OSTI2024

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

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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

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

Designing, commissioning, and retrofitting HVAC control systems for energy efficiency is crucial, but the use of ad-hoc control sequences by designers and contractors, based on scattered information, results in diverse and sub-optimal sequences. ASHRAE Guideline 36 (G36) addresses the challenge by providing standardized, rule-based HVAC control sequences that prioritize energy efficiency. However, there is limited evaluation of their energy performance at the building level, with only a few studies primarily focused on HVAC airside systems in small-to-medium-sized commercial buildings. In this study, the energy performance of ASHRAE Guideline 36 control sequences was assessed using a large office building emulator in Chicago. The emulator features a central plant system with multiple chillers and boilers as well as multiple variable air volume (VAV) systems with terminal reheat. To achieve a high-fidelity representation, we developed a Spawn-of-EnergyPlus-based model for the large office building, maintaining the DOE prototype large office building setup but substituting the HVAC system with its Modelica counterpart. This substitution ensures that the building thermal load, HVAC system's dynamics, and detailed control sequences are all accurately represented. The study involved evaluating and implementing control strategies outlined in ASHRAE Guideline 36-2021 to replace conventional controls. These strategies include the demand-based supply air temperature and duct static pressure setpoint reset and the request logic for demand-based reset of chilled/hot water supply temperature setpoints and pipe static pressure setpoints. Energy performance was evaluated under various load conditions, including cooling, heating, and transitional seasons, both for individual control strategies and in combination. The results indicate that the collective control strategies retrofit yield greater energy savings than the sum of individual strategies, highlighting the synergistic benefits of incorporating both airside and plant-side control retrofits. Additionally, energy savings of up to 41% in the heating season, 18% in the shoulder season, and 20 % in the cooling season were observed compared to baseline control while maintaining the thermal comfort level.

ASHRAE Guideline 36, Commercial buildings, Control↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2020

The ASHRAE Great Energy Predictor III competition: Overview and results

In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. Furthermore, the competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Reducing Risk With ASHRAE Standard 241

ASHRAE Standard 241’s requirements can make indoor spaces more resilient to the spread of airborne infectious diseases. Buildings that comply with ASHRAE Standard 241-2023, Control of Infectious Aerosols,1 will have the capacity to respond during times of elevated risk with systems that provide the necessary equivalent clean airflow for infection control (VECAi). To reduce risk effectively, these systems need to operate properly. This column explores Standard 241’s requirements for operations and maintenance in high-risk times (infection risk management mode or IRMM) and the importance of creating a Building Readiness Plan.

ashrae standard 241, Operations and Maintenance, b↗
DOE OSTI2026

Analysis of strategies to meet ASHRAE S241 infectious aerosol control targets by space type and region using EnergyPlus™ simulations

Professional organizations, such as ASHRAE, have recently proposed new voluntary standards for controlling infectious aerosols. Specifically, ASHRAE Standard 241 (S241) defines equivalent clean air targets for a range of space types that can be met through combinations of mitigation measures. This paper seeks to inform the selection of measures by space type and climate zone through building simulations that quantify the impacts of increased outdoor air ventilation; increasing filtration and/or adding germicidal ultraviolet (GUV) in the central heating, ventilation, and air-conditioning (HVAC) systems; or using portable air cleaners (PACs), upper room GUV, or whole room GUV approaches to increase equivalent clean air delivery. The measures are assessed individually and in practical combinations for their ability to meet S241 in four space types (offices, classrooms, dining areas, and healthcare waiting rooms) using prototype buildings modeled using EnergyPlus. The mitigation measures are compared holistically against baseline building operations using metrics for equivalent clean air, energy, and comfort. The results in this study show that single measures can meet S241 for offices with minimal impacts on energy and comfort, while either upper room GUV systems or combinations of measures such as MERV 13 HVAC filtration with PACs are needed to meet S241 for classrooms. The dining area and healthcare waiting room targets cannot be met in this study when assuming design occupancy and MS2 as the challenge agent, even when combining multiple measures together. This paper provides valuable considerations when designing measures to meet S241 for a range of spaces and scenarios.

GUV↗
DOE OSTI2019

Development of control quality factor for HVAC control loop performance assessment I—Methodology (ASHRAE RP-1587)

This article is the second paper from the ASHRAE research project RP-1587, focusing on the methodology of obtaining a control quality factor (CQF). This article presents a development of two CQFs for assessing the heating, ventilation, and air-conditioning (HVAC) control loop performance. Both CQFs are able to detect whether the control loop is able to maintain the setpoint and identify the loop's ability to handle disturbances. In addition, the reversal behaviors are assessed as well. The first CQF (CQF-Harris) is proposed based on the normalized Harris index using the recursive least squares method. This recursive least squares method is selected because of its computational efficiency compared with the maximum likelihood estimation method. The second CQF (CQF-EWMA) is based on the exponentially weighted moving average of the error ratio. The assessment scale of excellent, good, fair, bad, and failed, which indicates the quality of the HVAC control loops, is established as well. The sensitivity analysis for both CQFs is also conducted, and it provides insights on choosing the appropriate parameters to compute such CQFs. Such parameters include the sampling frequency, the length of the moving window, and the variance of the unmeasured disturbance. The field evaluations and tests of the proposed CQFs for simulated control loops and real control loops can be found in the companion paper with the title 'Development of Control Quality Factor for HVAC Control Loop Performance Assessment - III: Field Testing and Results (ASHRAE RP-1587).'

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Development of control quality factor for HVAC control loop performance assessment—II: Field testing and results (ASHRAE RP-1587)

This article is the third paper from the research project RP-1587, focusing on presenting a comprehensive field test of the proposed control quality factors (CQFs; i.e., CQF-Harris and CQF-exponentially weighted moving average [EWMA]) and testing results. Firstly, the simulated control loops and real control loops are evaluated for offline assessment. Then, the field experiment implemented for different HVAC control loops is assessed online using the proposed CQFs. Test results show that the proposed CQFs are capable of adequately and effectively assessing the HVAC control loop performance. The methodology of obtaining those CQFs is provided in the companion paper: Development of Control Quality Factor for HVAC Control Loop Performance Assessment I - Methodology (ASHRAE RP-1587) (Li et al. 2019 Li, Y., Z. D. O'Neill, and X. Zhou. 2019. Development of control quality factor for HVAC control loop performance assessment I - Methodology (ASHRAE RP-1587).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Update of ASHRAE Standard 140 Section 5.2 and Related Sections (BESTEST Building Thermal Fabric Test Cases)

This report documents new work intended to update the current building thermal fabric software-to-software comparative test specifications and example results of ANSI/ASHRAE Standard 140-2017, Sections 5.2.1, 5.2.2, 5.2.3, and related supporting sections. The Building Energy Simulation Test and Diagnostic Method (BESTEST) test specification and example results that provide the basis for these Standard 140 sections were originally published in 1995. The state of the art in building energy simulation modeling has advanced since then. Additionally, comments on the test specification over the years highlighted ambiguities, further necessitating the update to the original work. This new work was conducted for the U.S. Department of Energy (DOE) by Argonne National Laboratory (Argonne) and National Renewable Energy Laboratory (NREL), in collaboration with a working group of international experts associated with the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Standing Standard Project Committee (SSPC) 140. SSPC 140 is responsible for ANSI/ASHRAE Standard 140. The new work, presented in this report, defined the test cases and specifications such that building energy simulation programs with time steps of one hour or less can perform the tests. Revisions to the test specification and update of example results follow the vetting process of iterative field trials established for previous BESTEST work that has been adapted for ASHRAE Standard 140.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

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

This report presents the initial field test procedure and results for ASHRAE Guideline 36 control logic. The US Department of Energy’s Oak Ridge National Laboratory’s (ORNL’s) two-story Flexible Research Platform (FRP-2) was selected as a test building. The rooftop unit (RTU) system serves as the building’s primary cooling system, and each variable air volume (VAV) box with an electric reheat coil serves as the building’s heating system along with RTU gas furnace. In this study, the control for the RTU and VAV box adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. Each story of the FRP-2 building has 5 office zones (thermal zones), for a total of 10 zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

End-Use Analysis of ASHRAE Standard 90.1-2022

These data dashboards present the data and findings from PNNL's detailed analysis of ASHRAE Standard 90.1 on US commercial new construction buildings. The dashboards support simulation-based assessment of U.S. commercial buildings compliant with ASHRAE Standard 90.1-2004, 2007, 2009, 20210, 2013, 2016, 2019, and 2022.

Nambiar, Chitra↗
NASA NTRSDate not supplied

Using Time Series Data Products to Support ASHRAE’s Historic and Future Climate Data Needs through NASA’s POWER Web Services

To address the data needs for resilience and sustainability of building systems within ASHRAE, the NASA Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data on a long-term, global scale. Solar data from several NASA projects and meteorological data from NASA assimilation models have been reformatted and disseminated to the public via a user friendly web GIS-enabled based data portal (https://power.larc.nasa.gov) in selectable data formats, immediately amenable to key industry wide decision support tools such as EnergyPlus. Time series of potential climate conditions from downscaled climate simulations are being made available for similar statistics and formats. An emphasis is placed upon obtaining and utilizing time series data to conduct analysis of building system performance for current and planning building systems for energy savings and greenhouse gas emissions. This presentation has the learning following learning objectives for the ASHRAE community: 1) Learn how NASA POWER’s web service suite can provide supplementary solar and surface meteorological data parameters, including a discussion of uncertainty. 2) Learn how downscaled climate scenario information can be provided to evaluate potential future requirements. The presentation is part of a forum entitled, "Weather Data for Large-Scale Building Energy Modeling".

time series↗
DOE OSTI2020

Cost Effectiveness of ASHRAE Standard 90.1-2016 for the District of Columbia

This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2016. Moving to the Standard 90.1-2016 edition from Standard 90.1-2013 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cost Effectiveness of ASHRAE Standard 90.1-2016 for the State of Alabama

This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2016. Moving to the Standard 90.1-2016 edition from Standard 90.1-2013 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cost Effectiveness of ASHRAE Standard 90.1-2016 for the State of Alaska

This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2016. Moving to the Standard 90.1-2016 edition from Standard 90.1-2013 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cost Effectiveness of ASHRAE Standard 90.1-2016 for the State of Arkansas

This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2016. Moving to the Standard 90.1-2016 edition from Standard 90.1-2013 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cost Effectiveness of ASHRAE Standard 90.1-2016 for the State of Arizona

This report describes the methodology and results of a state cost-effectiveness analysis of ASHRAE Standard 90.1-2016. Moving to the Standard 90.1-2016 edition from Standard 90.1-2013 is found to be cost-effective for the state. Annual energy cost savings, added construction costs, and life-cycle costs are all described and presented here for various building types and climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗