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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Generating synthetic occupants for use in building performance simulation

Occupant behaviour simulation frameworks can employ synthetic populations to characterize occupancy and behavioural patterns in buildings based on observed demographic data at a certain geographical location. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing case study. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. Furthermore, we draw upon the extended version of drivers-needs-actions-systems (DNAS) framework to guide the selection of variables and data imputation. Our results show that the BN approach is powerful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on the multi-family housing particularly show better performance than the office and senior housing cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Robust anomaly detection for particle physics using multi-background representation learning

Abstract Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection (AD) for high-energy physics. First, rather than train a generative model on the single most dominant background process, we build detection algorithms using representation learning from multiple background types, thus taking advantage of more information to improve estimation of what is relevant for detection. Second, we generalize decorrelation to the multi-background setting, thus directly enforcing a more complete definition of robustness for AD. We demonstrate the benefit of the proposed robust multi-background AD algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Design and Field Testing of Manufacturable Advanced Low-Cost Receiver for Parabolic Trough Solar Power

This project addresses the 30-yr service life, ease of manufacturability for low cost, and low Operations and Maintenance (O&M) costs for our novel parabolic trough receiver, SunTrap. Current trough-based commercial power plants operate at 400°C or below, in part due to poor performance and lifetime of existing receivers at higher temperatures. Thermal losses from such receivers are dominated by radiation; by the Stefan- Boltzmann law, radiative losses have a component that scales as T 4, challenging high-temperature efficiency. Our novel SunTrap receiver offers increased reliability by eliminating the need to operate in a vacuum, while improving performance at higher temperatures by using high-performance insulation on exposed sections of the receiver that absorb negligible solar flux. As a result, SunTrap offers comparable optical efficiency to currently available technology while reducing thermal losses at higher temperatures. In this project, our novel receiver and all subcomponents have been designed to be compatible with molten salts to enable operation at next generation trough-based exit temperatures between 500 and 550°C. Norwich Technologies, with collaborators from Creare, Thermodyne, Solar Dynamics (previously Abengoa Solar), and the University of Louisiana – Lafayette successfully iterated the design of the SunTrap receiver through a series of modelling efforts, cost analyses, prototype builds, and performance and lifetime tests to minimize the levelized cost of energy (LCOE). The final design (1) realized predicted receiver performance in a series of field optical efficiency, laboratory thermal efficiency and freeze/thaw recovery tests, (2) optimized the receiver with respect to integration with standard collector systems, receiver-to-receiver connections, and receiver-to-mirror connections, (3) demonstrated 30-yr service life of receiver and all components of the receiver, (4) developed commercialization and manufacturing plans to enable rapid deployment, and (5) demonstrated durability of an air-stable solar-selective coating. NT’s disruptive technology marshals diverse advances in design, materials and coatings to revolutionize key aspects of the CSP trough solar field. By enabling significant operational and cost advances, this technology offers potential decreases in LCOE of over 20% compared to traditional parabolic trough systems.

14 SOLAR ENERGY↗
DOE OSTI2022

BTO Market Success Report 2015–2020

This report highlights 28 BTO-supported, technology-oriented research and development (R&D) projects that resulted in the launch of a commercial product, focusing on identifying new technologies or commercialization updates between 2015 and 2020, where the product remained on the market as of March 2021. The report also includes a listing of the full 45 commercial products supported by BTO as well as a listing of 101 lighting components that benefited from BTO support and were commercialized and integrated into finished lighting products during the same timeframe.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Collaboration for Advanced Modeling of Particle Accelerators

The pverarching purpose is to accelerate and expand the scope of discoveries from high energy physics (HEP) particle accelerators by enabling the design of accelerators that are significantly more compact and cheaper to build and run. This will be realized through (i) developing high-performance computing (HPC) accelerator and beam modeling capabilities to design the full range of systems required (ii) developing community simulation ecosystems that seamlessly integrate accelerator elements to facilitate the design and control of next-generation accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Advanced Rooftop Unit Control

This technical report documents the Advanced Rooftop Unit Control End Use Savings Shapes (EUSS) measure, including providing a description of the technology, modeling methods, and the energy savings calculated from applying the measure to applicable across the US commercial building stock using ComStock to quantify its potential. Advanced rooftop unit controls (ARC) generally consist of a supply fan VFD, and controls to implement demand controlled ventilation (DCV) and air-side economizing, as a retrofit to improve energy efficiency of existing rooftop units (RTUs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Prospects for Precision Neutrino Cross Section Measurements

The need for precision cross section measurements is more urgent now than ever before, given the central role neutrino oscillation measurements play in the field of particle physics. The definition of precision is something worth considering, however. In order to build the best model for an oscillation experiment, cross section measurements should span a broad range of energies, neutrino interaction channels, and target nuclei. Precision might better be defined not in the final uncertainty associated with any one measurement but rather with the breadth of measurements that are available to constrain models. Current experience shows that models are better constrained by 10 measurements across different processes and energies with 10% uncertainties than by one measurement of one process on one nucleus with a 1% uncertainty. This article describes the current status of and future prospects for the field of precision cross section measurements considering the metric of how many processes, energies, and nuclei have been studied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Modeled Performance of the Hathaway Solar Patriot house - Washington D.C

This Dataset contains field research raw data, analysis spreadsheet, photos, and final report from the Hathaway Solar Patriot House Building America Case Study project. This dataset details the monitored and modeled performance of a solar home outside of Washington, D.C. We modeled the home energy performance using DOE 2.2, performed numerous short-terms tests on the home, and monitored its occupied performance for 29 months. The home used modular construction, solar water heating, a ground-coupled heat pump, efficient appliances, and compact fluorescent lighting to reduce its energy consumption by 35% compared to the Building America research benchmark home. The addition of 6kW of photovoltaics (PV) increased the savings to 67% compared to the Building America research benchmark. A more efficient shell to reduce space conditioning loads would have brought the home closer to its zero energy goals. However, even with efficient lighting and appliances, the lights, appliance and plug loads were a significant energy consumer. About 4 kW of PV was required to meet the needs of these loads alone. To achieve the zero energy goal with no further efficiency increases, the Hathaway house would need about 2.6 kW of PV in addition to the 6.0 kW it now has. Applying advanced efficiency measures available or being developed, such as heat-recovery ventilation, superinsulation, and electrochromic windows, could reduce the heating, cooling, and domestic hot water (DHW) energy use to less than 1700 kWh per year, an 88% reduction in these loads from the Building America Benchmark. At this efficiency level, the appliance and plug loads come to dominate energy consumption and account for nearly 70% of the total energy use. This analysis points out that even with highly effective energy-savings technologies (pushed beyond levels currently practical), whole-house energy use reduction by efficiency measures is only about 60% without also reducing the energy use of appliances and plug loads largely considered outside the designers jurisdiction.

Array↗
DOE OSTI2023

Reducing Grid Costs while Abating Emissions: Opportunities for Flexible Building Loads

This study evaluates the value of technology-agnostic, shiftable flexible building loads in modeled 2030 and 2040 U.S. grids for four types of customers under three potential aggregated distributed energy resources programs. The value examined includes monetary value from providing grid services (e.g., energy, capacity, flexibility reserve, regulation reserve, contingency reserve) and from reducing greenhouse gas emissions. By comparing more than 845,164,800 simulated shifting opportunities, the study finds that the timing of consumption is critical for profit-driven customers. A program that is activated for 30 critical hours of system operation can result in to $73 per year in revenue for 1 kWh of shiftable load. Emission reduction, on the other hand, is best accumulated through a year-round program: 1 kWh of shiftable load can lead to up to 487.9 kg CO2e carbon reduction per year. The report also provides detailed insights on the trade-off between revenue and emissions, regional variation, short- versus medium- term value, and impacts from various building flexibility parameters, such as shifting window and dissipation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Preliminary Findings of the South Africa Power System Capacity Expansion and Operational Modelling Study: Preprint

Wind and solar power contract prices have recently become cheaper than many conventional new-build alternatives in South Africa and trends suggest a continued increase in the share of variable renewable energy (vRE) on South Africa's power system with coal technology seeing the greatest reduction in capacity, see 'Figure 6: Percentage share by Installed Capacity (MW)' in [1]. Hence it is essential to perform a state-of-the-art grid integration study examining the effects of these high penetrations of vRE on South Africa's power system. Under the 21st Century Power Partnership (21CPP), funded by the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has significantly augmented existing models of the South African power system to investigate future vRE scenarios. NREL, in collaboration with Eskom's Planning Department, further developed, tested and ran a combined capacity expansion and operational model of the South African power system including spatially disaggregated detail and geographical representation of system resources. New software to visualize and interpret modelling outputs has been developed, and scenario analysis of stepwise vRE build targets reveals new insight into associated planning and operational impacts and costs. The model, built using PLEXOS, is split into two components, firstly a capacity expansion model and secondly a unit commitment and economic dispatch model. The capacity expansion model optimizes new generation decisions to achieve the lowest cost, with a full understanding of capital cost and an approximated understanding of operational costs. The operational model has a greater set of detailed operational constraints and is run at daily resolutions. Both are run from 2017 through 2050. This investigation suggests that running both models in tandem may be the most effective means to plan the least cost South African power system as build plans seen to be more expensive than optimal by the capacity expansion model can produce greater operational cost savings seen only in the operational model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Simulation-based coefficients for adjusting climate impact on energy consumption of commercial buildings

This paper presents a new technique for and the results of normalizing building energy consumption to enable a fair comparison among various types of buildings located near different weather stations across the U.S. The method was developed for the U.S. Building Energy Asset Score, a whole-building energy efficiency rating system focusing on building envelope, mechanical systems, and lighting systems. The Asset Score is calculated based on simulated energy use under standard operating conditions. Existing weather normalization methods such as those based on heating and cooling degrees days are not robust enough to adjust all climatic factors such as humidity and solar radiation. In this work, over 1000 sets of climate coefficients were developed to separately adjust building heating, cooling, and fan energy use at each weather station in the United States. This paper also presents a robust, standardized weather station mapping based on climate similarity rather than choosing the closest weather station. This proposed simulated-based climate adjustment was validated through testing on several hundreds of thousands of modeled buildings. Results indicated the developed climate coefficients can isolate and adjust for the impacts of local climate for asset rating.

Asset Rating↗
DOE OSTI2020

Vertical meteorological patterns and their impact on the energy demand of tall buildings

In accordance with the developing economy and growing population, an increasing number of tall buildings have been constructed over the last 20 years. In 2017, there were 144 new buildings worldwide that were 200 m or greater in height; 50% of these buildings were in China. Due to the vertical gradient impact of meteorological parameters, the energy performance of tall buildings differs from that of general buildings. Few studies exist on vertical meteorological changes using measured data at different heights. Most studies on dynamic energy simulation simulate meteorological parameters using models. This study explores vertical meteorological patterns using hourly dry-bulb temperature, relative humidity, and wind speed data from 2007 to 2017 for a 325 m meteorological tower in Beijing. The temperature decreased significantly with increasing altitude (~0.9 °C per 100 m), while the daily temperature difference decreased with increasing altitude. The absolute humidity did not change significantly with height. The wind speed increased with altitude at approximately 2 m/s per 100 m. The building simulation showed that the annual heating load at a height of 320 m increased by 85% from that at 8 m; the annual cooling load decreased by 20%. Such significant differences in thermal loads for 300-m-tall buildings confirm the need to consider vertical meteorological differences in building performance simulations for tall buildings. A greater number of measurement points at different heights improve the simulation accuracy. Finally, guidance on selecting the heights for vertical metrological measurements is provided based on the influences of building thermal loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗
DOE OSTI2018

Are Ducted Mini-Splits Worth It?

The project discussed in this report was a collaborative effort between the Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL). For this project, PNNL and NREL developed a prototypical simulation model for ducted minisplits based on a combination of previously modeled DHP systems and central duct system models. To understand the implications of how this model would react under different scenarios, the DMS model was then used in 5 different prototypical homes, in three different cities, each representing one of BPA’s heating zones. Electric resistance zonal heat, with window air conditioning was used as a baseline for comparison in all cases. Only one manufacturer was modeled due to budget constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

The Institute for Sustained Performance, Energy, and Resilience

The Green Queue framework is a framework designed to automate the development and deployment of customized architecture- and application-aware power savings recipes for large-scale HPC applications achieving up to 21% and 32% energy savings on HPC production applications run at scale. Additional work focused on the memory sub-system involved our methodology that uses application and machine characterization information to build predictive machine learning models that can accurately quantify phase-level sensitivity to the reduced per core memory bandwidth resulting from changes in the memory bus frequency to reduce the power. We evaluated the predictive capability of the model on real applications and validated them at a fine grain level by looking at 43 individual computational phases or application hotspots as well as the whole application. For more than 91% of the application hotspots, the prediction error is less than 10% (15) . Building from these validated performance and power models collaborations among SUPER team members developed an automated end-to-end system to reduce the complexity of developing and deploying machine learning models for performance, power, and energy. The new framework Automatic Multi-objective Modeling with Machine Learning (AutoMOMML) enabled multi-objective optimizations (power and performance) for HPC workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Open-Source High-Fidelity Aggregate Composite Load Models of Emerging Load Behaviors for Large-Sale Analysis

The goal of this project is to accurately model the dynamic loads for power system planning and operation and to address the challenges inherent in such modeling. Existing load models are inadequate to handle the increasing proliferation of residential air conditioner loads and distributed energy resources, the emerging trends of price-responsive demand, and the growing importance of protective devices in equipment and buildings. A need has emerged to develop a fundamentally new class of load models and next-generation data tools. This work proposes to develop a set of regional–level, scalable open source load models and tools, including large-scale aggregate load protection, concept of price responsive demand, advanced load composition data, next-generation load model data tools, and advanced composite load model calibration and validation frameworks to address new challenges in the representation of next-generation load models.

24 POWER TRANSMISSION AND DISTRIBUTION↗