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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparison of effectiveness and energy use of airborne pathogen mitigation measures to meet clean air targets in a prototypical office building

Organizations such as ASHRAE and the Centers for Disease Control and Prevention (CDC) have proposed guidelines for controlling infectious aerosols in buildings, which can be met through measures such as modified operation of the heating, ventilation, and air-conditioning (HVAC) system or incorporating air-cleaning technologies. However, more research is needed to understand the trade-offs between health, energy, and comfort aspects when designing measures for these guidelines. To address this gap, this paper presents an analysis using new models for air-cleaning technologies, including in-duct and in-room germicidal ultraviolet (GUV) systems and portable air cleaners (PACs). These models are incorporated into an existing prototypical office building model and six measures are designed to meet ASHRAE Standard 241 and CDC clean air targets: MERV 13 HVAC filtration, maximum outdoor air supplied to the building, PACs, and in-duct, upper-room, and whole-room GUV. The measures are simulated for an office building in a cool and humid climate compared against a baseline simulation using MERV 8 filtration. The results show that all measures, except for the maximum outdoor air case, can meet the ASHRAE 241 standard without significant impacts on energy or comfort. The HVAC system measures were not able to meet the CDC target with the default system sizing and lead to significant energy increases, while the in-room measures were able to meet the CDC target with small impacts on energy consumption. Finally, this paper consolidates the simulation findings and provides practical guidance for building operators to meet clean air targets while limiting energy and comfort impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Exergy Analysis of a Convective Heat Pump Dryer Integrated with a Membrane Energy Recovery Ventilator

To increase energy efficiency, heat pump dryers and membrane dryers have been proposed to replace conventional fossil fuel dryers. Both conventional and heat pump dryers require substantial energy for condensing and reheating, while “active” membrane systems require vacuum pumps that are insufficiently developed. Lower temperature dehumidification systems make efficient use of membrane energy recovery ventilators (MERVs) that do not need vacuum pumps, but their high heat losses and lack of vapor selectivity have prevented their use in industrial drying. In this work, we propose an insulating membrane energy recovery ventilator for moisture removal from drying exhaust air, thereby reducing sensible heat loss from the dehumidification process and reheating energy. The second law analysis of the proposed system is carried out and compared with a baseline convective heat pump dryer. Irreversibilities in each component under different ambient temperatures (5–35 °C) and relative humidity (5–95%) are identified. At an ambient temperature of 35 °C, the proposed system substantially reduces sensible heat loss (47–60%) in the dehumidification process, resulting in a large reduction in condenser load (45–50%) compared to the baseline system. The evaporator in the proposed system accounts for up to 59% less irreversibility than the baseline system. A maximum of 24.5% reduction in overall exergy input is also observed. The highest exergy efficiency of 10.2% is obtained at an ambient condition of 35 °C and 5% relative humidity, which is more than twice the efficiency of the baseline system under the same operating condition.

Balaraman, Anand (ORCID:0000000218355788)↗
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 OSTI2024

End-Use Savings Shapes Measure Documentation: Demand Control Ventilation

This documentation focuses on a single end-use savings shape measure - Demand Control Ventilation (DCV). DCV can save energy by reducing the rate at which outdoor air (OA) is delivered during periods of less-than-design occupancy. This measure will enable DCV for air loops using applicable HVAC system types (all except dedicated outdoor air systems [DOAS], packaged systems, or that have an energy recovery ventilator [ERV]) and serving applicable space types (all except kitchens, dining areas, patient spaces, mechanical rooms, stairwells and corridors, or high exhaust space types) using model occupancy schedules to control the DCV. The measure is applicable to 72.7% of the stock floor area. As office buildings outside of California in ComStock are modeled using a single, whole-building space type, DCV is not applied to these building types. The DCV measure demonstrates 2.6% total site energy savings (119 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. Here, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Characterization of the indoor far-field aerosol transmission in a model commercial office building

As businesses gradually reopen and employees return to work, the potential spread of SARS-CoV-2 and its variants through airborne transmission via the heating, ventilation, and air-conditioning (HVAC) systems of commercial building raises concerns. Since the general practice in commercial buildings is to use low-efficiency air filters and given that indoor air is generally recycled, the degree to which cross-zone aerosol transmission occurs is of interest. To quantify the cross-zone aerosol transmission, experiments were conducted using a synthetic test aerosol in the five zones on the first floor of a model commercial office building at the Oak Ridge National Laboratory. Because the synthetic aerosol was tagged with fluorescent salt, the aerosol generated from the source zone can be distinguished from the background aerosols due to its unique fluorescent signal. Data from cross-zone campaigns showed that submicron-aerosol transmission was higher than the micron aerosols. In campaigns with doors closed, the submicron aerosol transmission was less than 16% and less than 11% for micron aerosol transmission. Finally, opening the interior doors that connecting different zones can significantly enhance the aerosol transmission for zones at the close proximity to the source, but has less impact on those farther away.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Energy-saving potential evaluation for primary schools with occupant-centric controls

Recent studies demonstrated that there is significant energy-saving potential for primary schools, which heating, ventilation, and air-conditioning (HVAC) systems with occupant-centric control (OCC) is an excellent candidate to save energy. However, such an energy impact has yet to be evaluated for different climate zones as well as different energy code versions, but is critical for technology transfer and deployment. Therefore, this paper conducts comprehensive evaluation on the energy-saving potentials for the primary schools with two advanced OCC strategies: presence-based and counting-based. In this work, ninety-six building energy models with stochastic behavior of occupants are developed and simulated, which consist of two building energy code versions, 16 climate zones, and baseline case (without OCC) and two advanced cases (with OCC). To evaluate the energy saving potentials for OCC, primary schools in the U.S. are used as an example. The results show that there is significant energy-saving potential for primary schools by considering OCC strategies, especially the counting-based case. The energy-saving potential is up to 10.2% for presence-based OCC and 12.41% for counting-based OCC. Furthermore, both climate and code version have a significant impact on energy savings from OCC strategies. The energy-saving potentials vary from 1.79% to 12.41% for different climates and code versions. This evaluation can also contribute to quantify the nationwide energy saving potential for other countries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Microbial sensor system for monitoring the environment and surfaces

A microbial sensor, system and method that can be used to determine the biochemical conditions of waters, saturated sediments, unsaturated soils, rhizosphere and other environments is disclosed. The flexibility of the microbial sensor design allows for the monitoring of surfaces (skin, ventilation conduits, etc.). An electrochemical microbial sensor system is composed of an indicator electrode(s) and a reference electrode. The reference electrode employs a hydrogen permeable membrane. The electrochemical system is interfaced into a signal/communication module allowing the manual or automated collection of data from field deployments and laboratory investigations. The data is transmitted using various communication technologies including Bluetooth™, cellular, satellite and radio telemetry to cloud-based data management systems. The stored data may be downloaded by users using open-source dashboard and visualization software to image the various environments and/or surfaces investigated.

Burge, Scott R.↗
DOE OSTI2021

From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control

Occupants are the core of the built environment. Traditional Heating, Ventilation, and Air-Conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC “Occupant-centric Control (OCC).” HVAC OCC strategy starts with collecting the occupant’s information (e.g., presence/absence) and then applies it to meet the occupant’s requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. Finally, we provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Nationwide HVAC Energy-Saving Potential Quantification for Office Buildings with Occupant-Centric Controls in Various Climates

The occupant-centric control (OCC) is receiving an increasing attention due to its ability to reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to investigate and quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings using a whole building simulation software EnergyPlus. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CBPM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.

Pang, Zhihong↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗
DOE OSTI2021

Coloured low-emissivity films for building envelopes for year-round energy savings

We report that buildings are responsible for over 40% of total US energy use, of which about 40% is directly related to the operation of heating, ventilation and air-conditioning (HVAC) systems. Saving energy to heat and cool buildings would contribute substantially to sustainability. Here we propose a category of coloured low-emissivity films for building walls that constitute the main component of the building thermal envelope. We demonstrate high reflectance (~90%) in infrared wavelength range and selective reflectance in the visible light wavelength range for desired colours. These films can help minimize radiative heat exchange between the indoor and outdoor environments, thus saving energy for all-year cooling and heating while satisfying the required aesthetical effect. Simulations show that these films can help reduce heat gain and loss by up to 257.6 MJ per installation wall area annually. In the case of a typical midrise apartment building, the HVAC system can save up to 124.46 GJ (equal to 9.87% of the building’s HVAC energy consumption). By rough estimation, a global CO 2 emission reduction of 1.14 billion metric tons annually could be achieved. Our work provides insights for innovative energy-saving building envelope materials that can help achieve global carbon neutrality and sustainability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Robust hierarchical dispatch for residential distribution network management considering home thermal flexibility and model predictive control

In the transactive energy (TE) paradigm, the devices of participative consumers, or prosumers, may be aggregated and employed to drive operational objectives at the network level. Home heating, ventilation and air-conditioning (HVAC) systems in particular are well-suited to modulate their behaviours based on both home thermal flexibility and requests from the utility grid. This paper develops a robust, hierarchical power dispatch scheme in the context of a residential distribution network. The formulation couples a unique, multiphase linear distribution optimal power flow (OPF) at the upper level with model predictive control (MPC)-based HVAC fleet controllers at the lower level. The proposed approach is tested on nearly 2000 homes with a three-phase distribution network in an intraday market setting, where two major applications are explored and analysed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Research challenges and directions in HVAC fault prevalence

This study provides a review of the current state of knowledge, gaps, and potential value in research on the prevalence of faults in commercial buildings. Two separate efforts were made in this study: (1) we performed a literature review to determine the extent of currently available fault prevalence data for heating, ventilation, and air-conditioning (HVAC) systems, and (2) we conducted dozens of interviews with subject matter experts and stakeholders to determine the HVAC fault data that would be of greatest value. Through the literature review and interviews, we discovered unmet needs for empirical data on the prevalence of faults at the desired level of granularity, consistency, and scale; this lack of data leads us to recommend future work studying commercial buildings’ HVAC fault prevalence, with robust fault taxonomy and a variety of meaningful fault prevalence metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Battery Equivalent Model for Residential HVAC

Flexible loads, especially heating, ventilation, and air-conditioning (HVAC) systems can be used to provide a battery-like service to the power grid by varying their demand up and down over a baseline. Recent work has reported that the round-trip efficiency for providing grid services with HVAC systems can be close to 1. Inspired by this promising result, in this work, we rigorously develop a battery equivalent model (BEM) for residential HVAC systems. Relying only on typically available data, the proposed BEM will bridge the observability and controllability gaps between utility companies and customers. The proposed BEM will enable the utility companies to manage the flexible HVAC devices as easy as charging/discharging the conventional energy storage devices. Validated physics-based models are used to train the BEM, whose prediction accuracy is illustrated through a simulation study.

Dong, Jin↗
DOE OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate

In residential buildings, rapid improvements in sensors, communication, and information technology have enabled occupancy-based building controls. These controls utilize occupancy information and modify the operation of the heating, ventilation, and air-conditioning (HVAC) system to minimize excess HVAC energy use, especially when the building is unoccupied. This reduces the total building energy consumption and utility bills while maintaining thermal comfort. In this paper, we present two novel occupancy-driven controls - reactive control and predictive control - and compare their performance. We model an all-electric residential community based on a 27- home community in Basalt, Colorado, in the United States. We simulated various scenarios, considering different temperature setback and control algorithms, to analyze the community-scale impact of these occupancy-based controls. The results show that total HVAC energy savings in a building ranges from 1%- 20% compared to the baseline scenario without occupancy-based controls. The energy-saving potential is highly correlated with the occupancy pattern and temperature setback in the building.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗