SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SEARCH · Engineering Papers
Results for “building data”
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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SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Tools Assessing Performance (FY2022 Quarter 4 Report)
We were finally successful in acquiring TTU’s 200-meter tower data for the duration of the Isolated Building Wake Experiment (IBWE). This data will provide an independent measurement of the undisturbed wind profile including heights far above those that are typical of meteorological sensor networks. These measurements will characterize the mean and turbulence throughout the atmospheric surface layer, which will be particularly useful for validation of highfidelity computational fluid dynamics models. This dataset can be shared freely among the TAP team but not outside of it as TTU sells this data to the wind power industry. The journal article overview has been submitted to LANL’s publication review and will be submitted to the Journal of Wind Engineering and Industrial Aerodynamics once the review is complete. We are nearing completion on another two articles. The first discusses analyses of the evolution of the measurements of mean wind speed along the centerline of the building as a function of atmospheric stability. Rather than using individual time steps, all of the wind speed measurements are normalized using the upwind measurement at 2 building heights (H) and filtered by wind direction and atmospheric stability. The second discusses the development of the diffusive wake model and uses the averaged profiles from the first article to validate the behavior of the model. The portion of the section below on the diffusive wake model discussing comparisons with the averaged profiles, will be included in the validation section of this paper.
17 WIND ENERGY↗
Cloud Impacts on Photochemistry: Building a Climatology of Photolysis Rates from the Atmospheric Tomography Mission
Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry– climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J -cloudy (standard, sometimes cloudy conditions) to J -clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.
actinic flux spectroradiometers↗
Exploratory analysis and performance prediction of big data transfer in High-performance Networks
Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.
97 MATHEMATICS AND COMPUTING↗
A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs
Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.
15 GEOTHERMAL ENERGY↗