Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “occupancy detection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Hawaiian Hoary Bat Report for Sandia National Laboratories, Kauai Test Facility

The Hawaiian Hoary Bat Report for Sandia National Laboratories, Kauai Test Facility (SNL/KTF) provides data and analysis of Hawaiian hoary bat occupancy of the SNL/KTF site. The monitoring period was from 2023-2025 and used passive acoustic detection at three locations to determine whether the Hawaiian hoary bat occurs at the site and if they roost and pup in the vegetation of the site. Bats were detected at all three locations, however, given the vegetation type at SNL/KTF, it is very unlikely

Jones, Amanda Kaye [Sandia National Laboratories (↗
DOE OSTI2020

Redox cycling-based detection of phenazine metabolites secreted from Pseudomonas aeruginosa in nanopore electrode arrays

The opportunistic pathogen Pseudomonas aeruginosa (P. aeruginosa) produces several redox-active phenazine metabolites, including pyocyanin (PYO) and phenazine-1-carboxamide (PCN), which are electron carrier molecules that also aid in virulence. In particular, PYO is an exclusive metabolite produced by P. aeruginosa, which acts as a virulence factor in hospital-acquired infections and is therefore a good biomarker for identifying early stage colonization by this pathogen. Here, we describe the use of nanopore electrode arrays (NEAs) exhibiting metal–insulator–metal ring electrode architectures for enhanced detection of these phenazine metabolites. The size of the nanopores allows phenazine metabolites to freely diffuse into the interior and access the working electrodes, while the bacteria are excluded. Consequently, highly efficient redox cycling reactions in the NEAs can be accessed by free diffusion unhindered by the presence of bacteria. This strategy yields low limits of detection, i.e. 10.5 and 20.7 nM for PYO and PCN, respectively, values far below single molecule pore occupancy, e.g. at 10.5 nM < n pore > ~ 0.082 per nanopore – a limit which reflects the extraordinary signal amplification in the NEAs. Furthermore, experiments that compared results from minimal medium and rich medium show that P. aeruginosa produces the same types of phenazine metabolites even though growth rates and phenazine production patterns differ in these two media. Here, the NEA measurement strategy developed here should be useful as a diagnostic for pathogens generally and for understanding metabolism in clinically important microbial communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Neutron diffraction study of magnetism in van der Waals layered MnBi 2n Te 3n+1

Two-dimensional van der Waals MnBi 2 n Te 3 n +1 ( n = 1, 2, 3, 4) compounds have been recently found to be intrinsic magnetic topological insulators rendering quantum anomalous Hall effect and diverse topological states. Here, we summarize and compare the crystal and magnetic structures of this family, and discuss the effects of chemical composition on their magnetism. We found that a considerable fraction of Bi occupies at the Mn sites in MnBi 2 n Te 3 n +1 ( n = 1, 2, 3, 4) while there is no detectable Mn at the non-magnetic atomic sites within the resolution of neutron diffraction experiments. The occupancy of Mn monotonically decreases with the increase of n . The polarized neutron diffraction on the representative MnBi 4 Te 7 reveals that its magnetization density is exclusively accumulated at the Mn site, in good agreement with the results from the unpolarized neutron diffraction. The defects of Bi at the Mn site naturally explain the continuously reduced saturated magnetic moments from n = 1 to n = 4. The experimentally estimated critical exponents of all the compounds generally suggest a three-dimensional character of magnetism. Our work provides material-specified structural parameters that may be useful for band structure calculations to understand the observed topological surface states and for designing quantum magnetic materials through chemical doping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Mortality among workers at the Rocky Flats Plant, 1951–2017

The Rocky Flats (RFs) Plant operated from 1951–1989 as part of the U.S. Department of Energy (DOE) nuclear complex. Its primary mission was weapons component fabrication, whereby workers were potentially exposed to radioactive and non-radioactive hazards. RF worker mortality was compared to the general population, and dose-response relationships between mortality and radiation organ doses were examined. RF workers first employed between 1951 and 1979 for ⩾30 d were identified (n = 9397). Vital status was determined using national and state death records up to 2017. Organ doses from external photons and neutrons irritation and internalised plutonium (Pu), americium (Am), and uranium (U) were modelled as cumulative lagged total doses per year. Beryllium exposure was evaluated as an effect modifier using data from the DOE Nationwide Beryllium Medical Program. Statistical analyses included standardised mortality ratios (SMRs), Cox proportional hazard models, and excess relative risk (ERR) models. Approximately 53.2% of workers were deceased by the end of the study. Nearly 90% were monitored for radiation exposure, with a mean weighted absorbed dose of 59.0 mGy for the lungs. Nearly 45% of workers had intakes of alpha-particle emitting radionuclides, and 46.7% were monitored for neutrons. Leading causes of death included ischemic heart disease (n = 999) and lung cancer (n = 361). The highest SMRs were observed for berylliosis (SMR: 176.9; 95% CI: 76.2, 348.7; n < 10) and asbestosis (SMR: 4.65; 95% CI: 2.23, 8.55; n = 10). Dose-response analyses showed no statistical increase in risk from low-dose radiation including lung cancer (ERR per 100 mGy: −0.02; 95% CI: −0.11, 0.08; n = 361) and Parkinson’s disease (ERR per 100 mGy: 0.13; 95% CI: −0.26, 0.31; n = 57). Approximately 45% of workers were monitored for beryllium, with a weak non-significant indication of effect modification for lung cancer risk. The RF cohort showed no evidence of a statistically significant increase in mortality from occupational radiation exposure. However, this study was limited by low statistical power, which inhibits the ability to detect effects. Future pooling of Million Person Study (MPS) cohorts will provide further insights, particularly regarding Pu as a carcinogen.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

What's in a Name? Developing a Standardized Taxonomy for HVAC System Faults

Faults occurring in heating, ventilation and air-conditioning (HVAC) systems have significantly negative impacts on building energy consumption, occupant comfort, and indoor air quality. In the past thirty years, extensive research has been conducted on fault detection and diagnostics (FDD) methods, and there are now dozens of commercially available FDD software tools. Growing adoption of FDD tools has the potential to generate a massive and useful data set on fault characteristics. However, the lack of a unifying taxonomy is a significant barrier to efficient analysis and evaluation of FDD outputs. Therefore, there is a strong need to develop a robust taxonomy which can better represent and interpret FDD output data. This paper documents the development of a unifying taxonomy for HVAC system faults in commercial buildings, with initial focus on air handling units, variable air volume terminal units, and roof top unit systems. The developed fault taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model as tools to better understand and support root cause analysis for HVAC faults. A variable air volume terminal unit is used as an example to demonstrate the application of the developed fault taxonomy. The taxonomy has short-term application in a major U.S. study on fault prevalence, and promises longer term benefits to FDD software developers and building operators by creating a foundation for improved approaches to identifying and resolving HVAC faults.

Chen, Yimin↗
DOE OSTI2020

Effects of trapped charge on the signal formation and detection efficiency for subsequent pulses in irradiated silicon sensors

Envisaged high energy physics experiments like the Future Circular Collider require unprecedented radiation hardness of the detectors, as well as short readout time due to high luminosity and occupancy. Silicon has proven to be extremely radiation hard. Sufficient signals can be recorded even at fluences close to 1 ∙ 1 0 16 n e q / cm 2 . Here, the signal formation was studied in silicon strip sensors, irradiated and annealed until the phenomenon of charge multiplication occurred. ATLAS12EC R0 mini sensors were tested by means of edge TCT measurements at temperatures around –20 °C. It was observed that the detection of subsequent signals separated up to several microseconds is altered by the charge trapped during the preceding pulses. The effects of trapped charge on the electrical configuration of a sensor are well known as a polarization or pumping effect in larger band-gap materials like diamond, but is often neglected for silicon at this relatively high temperature. The investigation of the effect created by trapped charges in silicon sensors using subsequent pulses allows to gain information on important parameters such as de-trapping times. Furthermore, it shows a severe impact on the sensor performance in a pile-up scenario. The irradiation fluence and hence the effective doping concentration, the temperature and the amount of initially created charge have a large impact on this phenomenon. The measurements presented help to characterize this phenomenon; particular attention was paid to the application point of view.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The CMOS Pseudo-Thyristor: a zero-static current discriminator circuit

We report a very low power discriminator circuit for pixelized detectors, called the Pseudo-Thyristor is described in this document. It is a positive feedback topology using regular PMOS and NMOS field-effect transistors (FET's) with zero static current. When a small charge is injected into the circuit, it flips rapidly due to the positive feedback and outputs a logic transition for further digitization. Simulation shows that in a 65 nm process, it is possible to achieve a detecting threshold below 5 fC while maintain the average power consumption below 10 micro-Watts when the hit occupancy is <10% for 40 MHz operation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

East Tennessee Technology Park 2006 Illness and Injury Surveillance Report

The U.S. Department of Energy’s (DOE) commitment to assuring the health and safety of its workers includes the conduct of illness and injury surveillance activities that provide an early warning system to detect health problems among workers. The Illness and Injury Surveillance Program monitors illnesses and health conditions that result in an absence, occupational injuries and illnesses, and disabilities and deaths among current workers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Gaining Real-Time Water Leak Detection

Devens Reserve Forces Training Area is a United States Army Reserve (USAR) Installation that struggles with severe water leaks, often causing significant damage to the facility and requiring major renovation. Traditional water use is highly dependent on occupancy, so it can be difficult to benchmark a facility’s water use. It can be exceptionally difficult when occupancy is transient and/or varies. Pacific Northwest National Laboratory (PNNL) collaborated with Devens to implement real-time monitoring of their water consumption by utilizing the smart meter data from their existing 23 water meters. PNNL created a simple algorithm to calculate hourly water consumption and trigger an alert to be instantly emailed to Devens’ personnel when there appears to be a water leak in any building with a smart water meter. Here, this approach is expected to save hundreds of thousands of dollars in unnecessary water consumption costs and damages from leaks and was implemented with little-to-no costs or service disruptions. Next steps for this project include slow leak detection through nighttime monitoring and to extrapolate this water leak approach to the remainder 360 water meters on USAR’s Enterprise Building Control System so USAR sites across the country can be instantly notified of potential water leaks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Tuning of charge density wave transitions in LaAu x Sb 2 by pressure and Au stoichiometry

Two charge density wave transition can be detected in La Au Sb 2 at ~ 110 and ~ 90 K by careful electrical transport measurements. Whereas control of the Au site occupancy in La Au x Sb 2 (for 0.9 ≲ x ≲ 1.0 ) can suppress each of these transitions by ~ 80 K , the application of hydrostatic pressure can completely suppress the lower transition by ~ 7.5 kbar and the upper transition by ~ 17 kbar . Here, clear anomalies in the resistance as well as the magnetoresistance are observed to coincide with the pressures at which the charge density wave transitions are driven to zero.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Occupational and dietary differences in hydroxylated and methoxylated PBDEs and metals in plasma from Puget Sound, Washington, USA region volunteers

BACKGROUND: Electronic waste (E-waste) recycling is a rapidly growing occupation in the USA with the potential for elevated exposure to flame retardants and metals associated with electronic devices. We previously measured polybrominated diphenyl ethers (PBDEs) in plasma from E-waste workers and found them similar to non-E-waste workers. OBJECTIVES: This study focused on structurally related PBDE derivatives, the hydroxylated (OH-PBDEs) and methoxylated (MeO-PBDEs) forms along with metals known to occur in E-waste. Humans can metabolize PBDEs and some MeO-PBDEs into OH-PBDEs, which is a concern due to greater health risks associated with OH-PBDEs METHODS: We measured 32 different OH-PBDEs and MeO-PBDEs in plasma samples provided by 113 volunteers living in the greater Puget Sound region of Washington State, USA. We measured 14 metals in a subset of 10 E-waste and 10 non-E-waste volunteers. Volunteers were classified based on occupational and dietary habits: work outdoors and weekly consume = 5 seafood servings (outdoor), electronic waste recycling (E-waste) or non-specific indoor occupations (indoor). A two-week food consumption diary was obtained from each volunteer prior to blood sampling. RESULTS: OH-PBDEs were detected in all volunteers varying between 0.27 – 102 ng/g/g-lipid. The MeO-PBDEs were detected in most, but not all volunteers varying between 0 – 60.4 ng/g/g-lipid. E-waste recyclers had OH-PBDE and MeO-PBDE plasma levels that were similar to the indoor group. The outdoor group had significantly higher levels of MeO-PBDEs, but not OH-PBDEs. Comparison of plasma concentrations of BDE-47 with its known hydroxylated metabolites suggested OH-PBDE levels were likely determined by biotransformation and at least two subpopulations identified differing in their apparent rates of OH-PBDE formation. The metals analysis indicated no significant differences between E-waste workers and non-E-waste workers. CONCLUSIONS: Our results indicate E-waste workers do not have elevated plasma levels of these contaminants relative to non-E-waste workers with similar dietary habits.

Schultz, Irvin↗
DOE OSTI2024

IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes

This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Learning-based CO 2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗