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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 OSTI2020

Smart Ventilation for Advanced California Homes

This project investigated smart ventilation approaches to minimize energy use for providing indoor air quality (IAQ) in high performance new California homes. Evaluation criteria included annual ventilation-related energy, peak energy and time-of-use savings, and the indoor air quality relative to a minimally code-compliant ventilation system. The simulations used CONTAM’s air flow and contaminant transport model, combined with the EnergyPlus building loads model. House types representing the default California Energy Code compliance homes were investigated for four California climate zones, covering a wide range of climate types. Both single and multi-zone smart ventilation controls were investigated. Contaminant sources included contaminants emitted continuously and varying with time, temperature and relative humidity, episodic emissions from occupant activities and outdoor particles. Single-zone ventilation controls that varied ventilation depending on outdoor temperatures were able to consistently save half of ventilation-related energy without compromising long-term IAQ. Ventilation strategies that tracked occupancy were less successful, because this work included generic contaminants with constant background emission rates. Energy performance for occupancy controls improved with a one-hour pre-occupancy flush out strategy. The addition of zoning ventilation controls did not offer significant IAQ to energy improvements compared to non-zonal versions of the same ventilation system type. The best controls had HVAC energy savings of 10-20%, with individual cases reaching up to 40% savings. However, these savings cannot be achieved without worsening personal exposures for at least one contaminant. A metric is needed to assess the competing changes in exposure to different contaminants in order to determine the net-health impacts of a control strategy. Controls that directly sensed contaminants and controlled them to acceptable levels showed that the California OEHHA limit for formaldehyde completely dominates system performance, with homes not able to meet the limit even with continuous operation of a fan sized to twice the current code minimum.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Smart Ventilation Controls for Occupancy and Auxiliary Fan Use Across U.S. Climates

Smart Ventilation has been developed as a way to reduce the energy associated with ventilation by changing when ventilation happens and how much ventilation occurs at any given time. In high performance buildings with low envelope and appliance related loads, ventilation is becoming a bigger part of the total building energy use and needs to be addressed if performance targets are to be met. This work explores the development and performance of smart ventilation controls based on occupancy and auxiliary fan operation that provide annual dwelling unit ventilation equivalence to ASHRAE Standard 62.2-2016. A prototype high performance home compliant with U.S. DOE Building America Zero Energy Ready program requirements was simulated using the REGCAP tool with and without smart controls across 15 U.S. DOE climate zones. Balanced and unbalanced IAQ fans were independently simulated, and all smart controlled fans had airflows double the reference 62.2-2016 airflow. Three idealized occupancy patterns were examined: 1st shift (a typical daily work/school absence), an extended 1st shift with more time spent out of the home evenings and weekends and 3rd shift (night work). The Occupancy controller turns the IAQ fan off during unoccupied periods, and it resumes ventilation upon their return. While unoccupied, contaminants are allowed to accumulate in the space, because occupants are not exposed to these contaminants. When occupants return home, they are exposed to these higher contaminant concentrations, and the controller increases the ventilation rate sufficiently to ensure equivalence with a continuous IAQ fan. Accounting for pollutant emissions that occur during unoccupied periods (as required by ASHRAE 62.2-2016), sharply distinguishes our occupancy controls from past Demand Controlled Ventilation systems. This new accounting method results in equivalent contaminant exposure, as well as lower reductions in average ventilation rates and lower energy savings. Smart controls were demonstrated that saved HVAC energy (i.e., avoided heating/cooling load, as well as fan energy)—averaging between 6 and 46% of ventilation-related energy use depending on the control strategy and occupancy pattern assumptions. The greatest savings were in the combined Auxiliary Fan + Occupancy control. Energy savings increased with climate heating demand and longer unoccupied time periods. The 3rd shift occupancy pattern had better performance, due to the thermal benefit of reducing the ventilation rate during the cold nighttime hours. This same effect provided a cooling energy benefit in hotter locations for the 1st shift. Overall, savings from occupancy-based smart controls were low, because of the recovery period required after occupants return home, during which the airflow is double the 62.2 reference. This recovery is required to maintain equivalence with the ASHRAE standard. Occupancy-based control performance was improved when combined with sensing auxiliary fans and when providing a pre-occupancy flush out of one- or two-hours. Performance was similarly improved if the ASHRAE Standard were to recognize that pollutant emissions are lower during unoccupied periods, iii allowing a lower target ventilation rate during unoccupied periods (not currently in the standard) (see Full vs. Half AEQ in this report). Finally, over-sized unbalanced fans that are cycled on-and-off by a smart controller (or timer) were found to substantially increase annual average air exchange and energy use relative to a continuous unbalanced fan due to the effects of superposition with natural infiltration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Weather responsive smart ventilation system using multiple optimization parameters

A smart ventilation system which uses outdoor temperature and moisture to optimize control of the system. The main principle is to shift ventilation from time periods that have large indoor-outdoor temperature and moisture differences to periods when these differences are smaller, and their energy and comfort impacts are expected to be less. Fan flow rates are reduced when the outside temperature and moisture falls outside of optimum levels, yet overall air exchange is maintained to ensure chronic and acute exposure to pollutants remains relative to best practice. Online weather and smart thermostat data can be used as control inputs, so no specific measurement devices are needed to control ventilation fans.

Parker, Danny↗
DOE OSTI2020

Smart Ventilation Controls Boost Energy Efficiency and Indoor Air Quality

The average American household spends more than $2,200 a year on energy, and heating, ventilating, and air conditioning (HVAC) costs comprise nearly half of the bill. This is one reason why home builders focus on tightening building envelopes to save energy. Yet, limiting the potential for air exchange can negatively impact indoor air quality (IAQ). When outdoor conditions are most extreme during occupied periods, there may be comfort implications from continuing high levels of ventilation during associated weather events. This is true even with heat recovery. To mitigate risks, the Florida Solar Energy Center developed and tested approaches for “smart” ventilation system controls that enable more reliable design, installation, and operation to achieve desired IAQ while also minimizing energy and comfort impacts.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Smart Ventilation Controls Boost Energy Efficiency and Indoor Air Quality

The average American household spends more than $2,200 a year on energy, and heating, ventilating, and air conditioning (HVAC) costs comprise nearly half of the bill. This is one reason why home builders focus on tightening building envelopes to save energy. Yet, limiting the potential for air exchange can negatively impact indoor air quality (IAQ). When outdoor conditions are most extreme during occupied periods, there may be comfort implications from continuing high levels of ventilation during associated weather events. This is true even with heat recovery. To mitigate risks, the Florida Solar Energy Center developed and tested approaches for “smart” ventilation system controls that enable more reliable design, installation, and operation to achieve desired IAQ while also minimizing energy and comfort impacts.

Building America↗
DOE OSTI2021

Development of Advanced Smart Ventilation Controls for Residential Applications

This study examined the use of zoned ventilation systems using a coupled CONTAM/EnergyPlus model for new California dwellings. Several smart control strategies were developed with a target of halving ventilation-related energy use, largely through reducing dwelling ventilation rates based on zone occupancy. The controls were evaluated based on the annual energy consumption relative to continuously operating non-zoned, code-compliant mechanical ventilation systems. The systems were also evaluated from an indoor air quality perspective using the equivalency approach, where the annual personal concentration of a contaminant for a control strategy is compared to the personal concentration that would have occurred using a continuously operating, non-zoned system. Individual occupant personal concentrations were calculated for the following contaminants of concern: moisture, CO2, particles, and a generic contaminant. Zonal controls that saved energy by reducing outside airflow achieved typical reductions in ventilation-related energy of 10% to 30%, compared to the 7% savings from the unzoned control. However, this was at the expense of increased personal concentrations for some contaminants in most cases. In addition, care is required in the design and evaluation of zonal controls, because control strategies may reduce exposure to some contaminants, while increasing exposure to others.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Field Validation of a Smart Energy Recovery Ventilation System Using Low-Cost Indoor Air Quality Sensors

This project is a field validation, using low-cost indoor air quality (IAQ) sensors, of a smart ventilation system that can help low-load homes in humid environments maintain acceptable indoor humidity conditions while providing adequate ventilation according to ASHRAE 62.2. The objectives of this research were to (1) address builders’ concerns with mechanical ventilation in humid environments and (2) answer the question of whether smart control logic helps with occupant comfort and the creation of a more acceptable indoor environment. To address the objectives of the study, the Southface team collected field data for one year in four Charleston, South Carolina, new construction homes in order to determine the differences in occupant comfort; comfort metrics; IAQ; and heating, ventilating, and air-conditioning (HVAC) energy consumption when toggling biweekly between an energy recovery ventilator (ERV) operating continuously and an ERV operating with smart, time-varying humidity control logic. The smart ventilation algorithm under consideration in this field test did create a less humid indoor environment on an annual basis as quantitatively measured through temperature and relative humidity (T/RH) readings, expressed most discernably as “percentage of time above 60% RH” and “percentage of time above 55°F dewpoint.” However, the difference it made was inconsistent during the spring, summer, and fall months, and it was only directionally consistent during the winter months. We suspect that this is primarily due to the long runtimes and concomitant dehumidification activity of the air-conditioning (A/C) units in response to the high sensible loads in Charleston. The effect of the smart ventilation algorithm was not discernable to the occupants in this study, as recorded through seasonal surveys.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Brilliant Execution of Smart Labs: Employing Best Practices to Improve Safety and Reduce Energy for Sustainable Labs: Preprint

Laboratory buildings often dominate the energy use on research campuses, consuming three to four times the energy of a typical office building. In most cases, these facilities are defined as “mission critical,” with excessive energy use as simply an unfortunate truth that cannot be avoided without compromising safety. However, case studies indicate cost savings opportunities of up to 40% in laboratories (compared to standard practices) by employing energy efficient design strategies that also improve the safety of lab spaces (Better Buildings 2018). To help stakeholders implement improvement measures, the Smart Labs Toolkit describes a proven approach to optimizing performance of laboratories and critical control environments in new or existing facilities. The Toolkit outlines distinct phases for building a successful Smart Labs program that include: Plan, Assess, Optimize, and Manage. Each phase has specific tasks, training videos, and resources to educate users, support the efforts of key stakeholders, and maximize benefits for the organization in implementing high-performance labs. The Toolkit also includes case studies from the 17 Better Buildings Smart Labs Accelerator partners, sharing lessons learned and supporting the Smart Labs approach to safe, efficient world class science. This paper reviews the technology choices for implementing Smart Labs ventilation strategies, controls, and other efficiency measures. Better Building Smart Lab Accelerator partners provide case study examples of safety and energy-savings results of using Smart Labs approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ushering in the New Age of Laboratories: Smart Labs in Practice; Preprint

Ventilation is the first line of defense against airborne hazards produced during research activities in laboratories. A vital component to maintaining healthy, safe, indoor air quality, laboratory ventilation systems are often victim to ineffective operation, posing a risk to an organization's most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. By providing a framework to improve the safety and energy efficiency through optimized ventilation and operations, the Smart Labs Toolkit guides laboratory stakeholders through a straight-forward, holistic approach to achieving a dynamic Smart Labs program. A Smart Labs program employs a combination of physical, administrative, and management techniques to plan, assess, optimize, and manage high-performance laboratories. Grounded in the Smart Labs methodology, the National Renewable Energy Laboratory (NREL) implemented a successful Smart Labs program to oversee the design, construction, maintenance, and operations of its laboratories. To accomplish this effort, NREL's key stakeholders created an internal partnership to align NREL's existing laboratories with Smart Labs principles and solidify organizational roles for the safe and efficient operation of laboratory assets. The program provides the groundwork for decarbonization strategies centered around building operation. This paper outlines best practices employed by NREL to develop a cross-cutting Smart Labs team, garner managerial support, and effectively communicate of goals around safety and energy. Strategies include specific Smart Labs best practices, such as implementing a Laboratory Ventilation Risk Assessment - a systematic process for identifying risk due to airborne hazards and informing dynamic, demand-based ventilation to optimize safety and efficiency.

decarbonization↗
DOE OSTI2020

Development of a Residential Smart Range Hood

This report documents the development testing of a residential smart range hood for automated mitigation of air pollutants generated during cooking events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Development of window scheduler algorithm exploiting natural ventilation and thermal mass for building energy simulation and smart home controls

Building energy simulations often rely on abstract assumptions when it comes to natural ventilation, such as ‘windows always open [or closed]’ or ‘windows open when outdoor temperature is below a certain threshold.’ However, simulations based on these assumptions fail to fully exploit the cooling potential of natural ventilation, as its effectiveness can be enhanced or diminished by various factors, including the presence of thermal mass. This issue also extends to smart home controls, where determining the window schedule becomes challenging without information about the building's response to outdoor conditions. To address these issues, this study has developed an analytical model for window operation schedules that leverages the passive cooling from natural ventilation. The analytical model was validated against a Modelica simulation. A case study utilizing the BESTEST model of ANSI/ASHRAE Standard 140 underwent validation with EnergyPlus simulations, showing strong concordance. The algorithm provides window schedule recommendations adapted to various airflow rates, thermal masses, and climate variations. Notably, the case study demonstrated that proper window scheduling could reduce indoor temperature by up to 8 °C under the given simulation settings, thereby improving resilience and indicating potential energy savings. Furthermore, the paper explores the potential opportunities and challenges this approach presents, especially for building simulation and smart home applications.

42 ENGINEERING↗
DOE OSTI2022

Work Smarter, Not Harder: Improving Energy Efficiency and Safety through Smarter Ventilation: Preprint

Ventilation is a key component to maintaining healthy, safe indoor air quality. Especially important in laboratories, ventilation is the first line of defense against airborne hazards produced during research activities. Though a vital component, laboratory ventilation systems are often victim to ineffective operation, posing a risk to the most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. To improve both energy efficiency and safety in laboratories, we present the Smart Labs Toolkit - a resource developed by the U.S. Department of Energy Federal Energy Management Program and the International Institute for Sustainable Laboratories that guides laboratory stakeholders through a straight-forward, holistic approach to achieve dynamic, high-performance laboratories. Smart Labs enable safe and efficient world class science to occur in laboratories through high-performance methods. A Smart Labs program employs a combination of physical, administrative, and management techniques to assess, optimize, and manage high performance laboratories. We will focus on a central component of the Smart Labs approach - the Laboratory Ventilation Risk Assessment, a systematic process for identifying risk due to airborne hazards to inform the operation of dynamic ventilation that optimizes safety and efficiency. Case studies of organizations who have successfully implemented Smart Labs ventilation management programs will also be shared. In learning ventilation strategies successful in critical laboratory environments, learn the tools and resources needed to successfully manage energy in any building through smarter, safer ventilation.

buildings↗
DOE OSTI2024

Brick Schema Standardized Plug Load Control Strategies for Load Reduction: Preprint

Plug loads comprise a significant percentage of commercial building energy consumption. Applying intelligent controls to turn off plug loads when unused can provide dynamic load reduction and flexibility, which are key traits of grid-interactive efficient buildings. This capability is important for equitable decarbonization as it can enable disadvantaged communities to electrify buildings without costly upgrades to electrical infrastructure. In this work, we present the effectiveness of various control strategies along with the operational lessons that informed their design. During a three-year period, we operated over 600 smart outlets in 12 university office buildings. The attached plug loads consisted primarily of printers, TVs, water dispensers, and copiers. After recording baseline power measurements for one year, we designed plug load control (PLC) strategies for each plug load type, use, and for different risk tolerance levels because PLC can potentially be disruptive to daily work. We used the Brick Schema to facilitate the management of plug load locations and other metadata. For advanced controls, we integrated the smart plugs with heating, ventilation, and air conditioning (HVAC) systems through the campus building automation system. We found static schedules to be the least disruptive and most predictable for occupants, resulting in 38% and 66% energy savings in two studies. For printers, print server-triggered PLC produced 86% savings, the highest of all strategies with minimal occupant impact. Scheduling of water dispensers and digital signage TVs produced 49% and 70% savings respectively with opportunities to improve performance with the use of HVAC occupancy data.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Laboratory Efficiency Strategies and the Smart Labs Program

Focusing on critical spaces, such as labs, will enable agencies to prioritize federal energy efficiency and decarbonization goals. FEMP's Smart Labs program is an example of emerging efficient laboratory building strategies. The benefits of this program include improved safety and health, reduced energy consumption and carbon emissions, lower operating costs, reduced degradation, and increased retention and recruitment of top talent researchers and sciences. In this session, with the help of our national lab partners, Sandia National Laboratory and Lawrence Berkeley National Laboratory, you will learn about the steps to implement a Smart Labs program of your own and the methods behind the high-performance laboratory building. The partners will share best practices in implementation, practical advice for building a team, and how to address these critical facilities.

decarbonization↗
DOE OSTI2020

A Novel Simulation-Based Framework for Sensor Error Impact Analysis in Smart Building Systems: A Case Study for a Demand-Controlled Ventilation System

Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Optimizing Ventilation Using Low-Cost Sensors to Improve Health, Safety, and Energy Efficiency

Air is the primary carrier of hazards within a space, whether it be hazardous byproducts of laboratory research activities or airborne pathogens. As a result, building ventilation is a primary defense against unseen airborne hazards. Critical laboratory facilities require effective mitigation of exposure to research-related, airborne hazards, providing a proving ground for effective ventilation strategies that optimize safety of occupants and reduce energy use. The heart of smart laboratory building operation is dynamic, analytics-based ventilation, which requires an in-depth intimate knowledge of building environmental conditions achieved through contaminant-detection systems. Unfortunately, currently many contaminant-detection solutions are expensive, elaborate systems that raise barriers for building managers. Through the successful deployment of a novel low-cost, modular sensor technology, we have developed a demand-control ventilation protocol effective in improving safety and reducing energy in critical laboratory environments. In this article, we will highlight best practices and lessons learned through this deployment that can be applied beyond laboratories. This article describes a low-cost sensor to support providing a safe, healthy building environment and reduce energy use through effective and efficient ventilation.

dynamic management of indoor air quality↗