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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Human-in-the-loop Sensing and Control for Commercial Building Energy Efficiency and Occupant Comfort

Most of the existing heating, ventilation and air conditioning (HVAC) systems in commercial buildings operate in a conservative manner by assuming maximum occupancy in each room during pre-specified periods of the week, leading to significant energy being wasted as rooms are over-conditioned compared to the actual requirements of the occupants. Though critical, our understanding of occupancy patterns and thermal comfort needs of the occupants in commercial buildings is lacking and it is well known that both of these quantities are stochastic and time-varying, thus requiring sensing solutions to estimate them. This project had the goal of designing, implementing and evaluating a hardware and software solution to ameliorate this challenge. In particular, a depth camera (one whose pixels reveal distance from the camera as opposed to color values) placed on doorways is used to detect entrance and exit events from thermal zones in the building, and thereby estimate their occupancy levels. This information is then fed to a novel control algorithm that can, through interactions with the HVAC system, learn how to provide control inputs that maximize comfort and minimize energy waste. The resulting system represents a significant improvement over existing controllers for commercial HVAC systems and allowed us to improve our understanding of the design of future human-in-the-loop control solutions. For this solution to be feasible, the project had target metrics for its performance and cost. In particular, entrance and exit events for occupants moving about the building would need to be detected with an accuracy higher than 97%; and the resulting control inputs derived from this information would need to lead to approximately 10% energy savings compared to a schedule-based controller. Furthermore, regarding the final hardware design, the project had a target bill of materials (BOM) cost for the sensing solution of less than US$200 per unit while using less than 25W of power on average. All of these target metrics were met or exceeded by our final proposed solution. We performed evaluations by deploying the system in over 20 rooms of different types across 6 commercial buildings in Pittsburgh, PA over the course of three years, and performing targeted controlled experiments to test its performance along the different metrics. The human-in-the-loop control solutions (both hardware and software) developed through this project are expected to lead to significant improvements in the comfort and energy efficiency of HVAC systems used in commercial buildings. The insights we developed through the project pave the way to HVAC systems that can condition interior spaces according to their real-time utilization and the thermal comfort needs of the occupants, thereby reducing energy use. They also open up a new learning-based way of configuring HVAC controllers without having to manually fine-tune them for each building. These innovations can significantly increase the adoption of novel control solutions by the industry and thereby save resources and reduce costs of operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Smoothed perturbation analysis algorithms for estimating the derivatives of occupancy-related functions in serial queueing networks

The authors present smoothed perturbation analysis (SPA) estimators for the derivative of a number of occupancy-related functions in serial queuing networks with finite buffer spaces. The functions are the average number of customers at a network as seen by an arrival, the probability that a customer is blocked at a particular queue, and the probability that a customer leaves a queue empty. In all three cases, the variable is a parameter of the distribution of service times at one of the queues. The derivative estimators considered are very simple and flexible, and they easily lend themselves to analysis of unbiasedness. Unlike most of the established SPA estimators, the present ones are not based on the computation of hazard rates.

Wardi, Y.↗
DOE OSTI2025

Energy impacts of nationwide window upgrades in commercial buildings

This report presents comprehensive estimates of the energy impacts of nationwide commercial building window upgrades in the United States, using a conservative approach. Windows play a substantial role in determining building energy use and occupant experience. Estimates point to commercial building windows impacting loads that represent more than 6 quads (approximately 6%) of annual primary energy use in the U.S. (Harris, 2022). Beyond heating and cooling loads, windows also have effects on lighting and occupant comfort. The fastest route to improving the energy efficiency of windows in U.S. buildings is upgrading or replacing windows in existing buildings. This is due to poor performance of windows in older existing buildings compared to most new construction, low levels of window replacement, and long window service life compared to energy-using building components. Nationwide window upgrades were considered using the following technologies: • Secondary glazing systems • Double pane (clear and tinted) • Triple pane (clear and tinted) • Electrochromic glazing Nationwide upgrades provide on the order of 4%–6% site energy savings in typical buildings, or up to 26% in buildings with the highest savings potential. Electrochromic windows, with their ability to adapt dynamically to environmental conditions, can provide additional benefits, ranging from median savings of 7.2% in buildings with window to wall ratio (WWR) greater than 10% and up to 28% for some buildings. Savings increase substantially for buildings with higher WWR. This study’s approach focused on isolating the direct energy benefits from improvement in window performance, and does not take into account the following additional benefits from window retrofits, which are likely to be substantial: • Managing peak demand and enabling HVAC equipment downsizing. • Energy savings from customizing upgrades to building type and climate. • Energy savings and comfort improvements resulting from post-retrofit reductions in air leakage. • Non-energy benefits, such as occupant comfort and resilience during extreme weather.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

From roads to roofs: How urban and rural mobility influence building energy consumption

In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Simulation, Challenge Testing and Validation of Solutions for Residential and Commercial Occupancy Sensing/Counting and CO2 Sensing (Final Report)

Carbon dioxide sensors used for monitoring and control applications in buildings are known to be sensitive to long term drift and can be affected by variations in temperature, humidity, other gas interferents, pressure and other factors that cannot be corrected through the typical auto-calibration methods often embedded in the sensor electronics. This investigation was part of the Category D defined in the ARPA-E Saving Energy Nationwide in Structures With Occupancy Recognition (SENSOR) Financial Assistance Funding Opportunity Announcement No. DE-FOA-0001737, FFDA No. 81.135, January 18, 2017. The FOA defined target criteria for the evaluation of the Category C commercial CO2 sensors selected for review and development through the ARPA-E SENSOR program and charged the Iowa State Category D team with developing an evaluation methodology for Category C sensors in accordance with these criteria. As part of the work, the Iowa State team chose to engage the standards community in the development of the evaluation methodology. Standard development work in the D22.05 Indoor Air and D22.03 Ambient Atmospheres and Source Emissions subcommittees of ASTM International Committee D22 on Air Quality on the evaluation of low cost sensors and provision of guidance for using indoor carbon dioxide concentrations to evaluate indoor air quality and ventilation is of particular relevance to the Category D efforts. To date, three draft standards with specific input by the Iowa State Category D team have passed the subcommittee balloting stage and are being balloted in December 2022 and January 2023 in the main D22 committee. In addition to the aforementioned standards to which the PI was a contributing author, intellectual property developed in this project pertaining to “An Automated System for Evaluation of the Long Term Performance of Carbon Dioxide Sensors” and “An Automated Ground Truth System for Real-Time Reliability Assessment, Capture of Control Decisions and Energy Savings Measurements for Occupancy Recognition Systems and Other Applications” are the other major deliverables of the project. These standards and the developed intellectual property are expected to benefit the public in a number of ways stemming from their provision of means to benchmark the performance of carbon dioxide and other sensor systems. In particular, there is high interest in techniques to demonstrate the performance of low-cost carbon dioxide sensors which can be used for typical HVAC applications (e.g., demand controlled ventilation, outdoor and indoor air monitoring, estimation of occupancy, etc.).

42 ENGINEERING↗
DOE OSTI2020

A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Sites

ABSTRACT Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs ( Sus scrofa ). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture–recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions. © 2020 The Wildlife Society.

Schlichting, Peter E.↗
NASA NTRS2001

Radiation breakage of DNA: a model based on random-walk chromatin structure

Monte Carlo computer software, called DNAbreak, has recently been developed to analyze observed non-random clustering of DNA double strand breaks in chromatin after exposure to densely ionizing radiation. The software models coarse-grained configurations of chromatin and radiation tracks, small-scale details being suppressed in order to obtain statistical results for larger scales, up to the size of a whole chromosome. We here give an analytic counterpart of the numerical model, useful for benchmarks, for elucidating the numerical results, for analyzing the assumptions of a more general but less mechanistic "randomly-located-clusters" formalism, and, potentially, for speeding up the calculations. The equations characterize multi-track DNA fragment-size distributions in terms of one-track action; an important step in extrapolating high-dose laboratory results to the much lower doses of main interest in environmental or occupational risk estimation. The approach can utilize the experimental information on DNA fragment-size distributions to draw inferences about large-scale chromatin geometry during cell-cycle interphase.

Chromatin/radiation effects↗
NASA NTRS1989

A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

Elfes, Alberto↗
DOE OSTI2021

A framework for estimating the energy-saving potential of occupant behaviour improvement

Energy-related occupant behaviour in buildings has demonstrated considerable energy-saving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models. This article proposes a holistic survey-and-simulation-based framework for estimating the energy-saving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement programme with effective and efficient measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator

Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. In conclusion, the derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.

42 ENGINEERING↗
NASA NTRS2019

Multi-Agency Annual Southeastern Beach Mouse Habitat Occupancy Monitoring

The southeastern beach mouse (Peromyscus polionotus niveiventris, hereafter SEBM), a federally threatened species was identified by Merritt Island National Wildlife Refuge (MINWR) as one of the top five priorities for management. The Kennedy Space Center (KSC) Environmental Management Branch was asked to provide support in the form of population monitoring for SEBM on KSC and the surrounding properties. When we began the project, the SEBM was known to occupy sites ranging from Volusia through Brevard and Indian River Counties, but the exact distribution within the Federal lands encompassing the Cape Canaveral Barrier Island Complex (CCBIC) was not well known. The broad range of habitat conditions encompassing the species’ historical range and the diversity of sites historically known to contain SEBM required a landscape-scale approach for both monitoring the population and for investigating species habitat relationships. We designed a monitoring plan for the SEBM with the goal of estimating the habitat occupancy of beach mice annually each winter/spring throughout the entire area of suitable coastal habitat (approximately 72 km linear distance along the beach). Methods were tested in a 2008 pilot study, and during 2010-2018 we monitored the proportion of coastal habitat occupied by the SEBM on the CCBIC. Monitoring was focused on using dynamic habitat occupancy surveys with data collected with rapid assessment techniques (i.e., track tubes or live-traps) to detect SEBM occurrence at sampling stations. An important component of the study design was the use of repeated samples that provided the information needed to estimate detectability. Measuring detectability was required to account for the fact that surveys could fail to detect animals at sites at which they were actually present (Mackenzie et al., 2002). This monitoring framework is very flexible, allowing numerous hypothesis to be tested about how various factors influence beach mouse occupancy using habitat, geographic, and environmental covariates. Our survey has been the longest survey conducted for any of the seven extant beach mouse subspecies and has encompassed nearly the entire remaining range of the SEBM. Although occupancy surveys of other beach mouse subspecies via track tubes have been conducted on the Gulf coast of Florida by Florida Fish and Wildlife Conservation Commission (FWC), United States Fish and Wildlife Service (USFWS), and various universities, we expanded previous methods to fit unique aspects of the SEBM, and thus our study was very different from any of the previous studies. To achieve this specificity, we employed an adaptive approach throughout the study, with all activities aimed at providing managers with information related to environmental compliance and natural resource management on KSC and the surrounding Federal lands.

Eric Douglas Stolen↗
DOE OSTI2022

Integrating Public Health Surveillance and Environmental Data to Model Presence of Histoplasma in the United States

In the United States, the true geographic distribution of the environmental fungus Histoplasma capsulatum remains poorly understood but appears to have changed since it was first characterized. Histoplasmosis is caused by inhalation of the fungus and can range in severity from asymptomatic to life-threatening. Due to limited public health surveillance and under detection of infections, it is challenging to directly use reported case data to characterize spatial risk. Using monthly and yearly county-level public health surveillance data and various environmental and socioeconomic characteristics, we use a spatio-temporal occupancy model to estimate latent, or unobserved, presence of H. capsulatum , accounting for imperfect detection of histoplasmosis cases. We estimate areas with higher probabilities of the presence of H. capsulatum in the East North Central states around the Great Lakes, reflecting a shift of the endemic region to the north from previous estimates. The presence of H. capsulatum was strongly associated with higher soil nitrogen levels. In this investigation, we were able to mitigate challenges related to reporting and illustrate a shift in the endemic region from historical estimates. This work aims to help inform future surveillance needs, clinical awareness, and testing decisions for histoplasmosis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

The Physical/Chemical Closed-Loop Life Support Research Project

The various elements of the Physical/Chemical Closed-Loop Life Support Research Project (P/C CLLS) are described including both those currently funded and those planned for implementation at ARC and other participating NASA field centers. The plan addresses the entire range of regenerative life support for Space Exploration Initiative mission needs, and focuses initially on achieving technology readiness for the Initial Lunar Outpost by 1995-97. Project elements include water reclamation, air revitalization, solid waste management, thermal and systems control, and systems integration. Current analysis estimates that each occupant of a space habitat will require a total of 32 kg/day of supplies to live and operate comfortably, while an ideal P/C CLLS system capable of 100 percent reclamation of air and water, but excluding recycling of solid wastes or foods, will reduce this requirement to 3.4 kg/day.

Bilardo, Vincent J., Jr.↗
DOE OSTI2024

Differentiable stochastic halo occupation distribution

ABSTRACT In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep reinforcement learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of halo occupation distribution (HOD) models that are used to connect galaxies with their dark matter haloes. Using a combination of continuous relaxation and gradient re-parametrization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogue realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalogue generated from the Bolshoi simulation using a standard HOD model and find near-identical posteriors as standard Markov chain Monte Carlo techniques with an increase of ∼8× in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Tool for Developing Integrated Strategies for Decontamination and Waste Management - 20291

Management of waste from chemical, biological, and radiological incidents is complicated by the fact that decontamination decisions have a profound impact on the characteristics of resulting waste streams. Wide-area biological and radiological incidents could produce massive quantities of waste that may need to be treated on-site and sent for subsequent disposal as non-contaminated materials, or else be directly disposed of as contaminated materials. The EPA has developed the Waste Estimation Support Tool (WEST) for characterizing and quantifying biological and radiological waste that may be generated from decontamination efforts. This paper focuses on the WEST's uses for radiological incidents. WEST combines Geographic Information System (GIS)-based analysis of externally-supplied plume data, infrastructure databases derived from the Federal Emergency Management Agency's (FEMA's) Hazus tool [1], and satellite imagery surface recognition algorithms to combine the composition and square footage of the buildings in the plume with estimates of the materials between the buildings in the plume. The resulting GIS data files are then imported into a Microsoft Access database application, where they are combined with information about the nature and concentration of contaminants, and then subjected to decontamination strategies for different contaminated surfaces. The tool provides estimates of the type and quantities of potential wastes resulting from simulated decontamination and/or demolition activities and includes estimates of the remaining contamination levels including residual contamination contained within each waste stream. Estimates are presented at several levels of detail, allowing users to obtain needed data at the desired resolution. These include estimates for the total affected area, estimates by contamination zone, estimates by decontamination method(s), and estimates by building type (occupancy classification). EPA is currently developing the next version of WEST which will include several substantial enhancements. The most significant improvement for the next version of WEST will include the ability for users to develop contamination scenarios and waste estimates based on previously developed, readily available, and geographically specific infrastructure data. Instead of using WEST's default infrastructure data based on FEMA's Hazus tool, users will be able to import their own building data specific to the geographically affected area. This capability may substantially decrease uncertainties in the resulting waste estimates because the results will be based on actual building data (numbers of each type, square footage, building height, etc.). Two other significant enhancements will be the ability to generate waste estimates for vehicles and biomass. In addition to building debris and building decontamination waste, vehicles and biomass will likely constitute a significant percentage of the total waste which may result from wide area contamination events. This presentation will present the most recent version of WEST, which includes such considerations as affected biomass (e.g., trees), vehicles, and the ability to replace the default Hazus infrastructure databases with custom infrastructure databases. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). Here we proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.

60 APPLIED LIFE SCIENCES↗
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↗