The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This light-duty vehicle inventory dataset provides information on vehicle registrations by vehicle type (car vs. truck), fuel type, and model year showing the changes in adoption trends over time and average fuel economies. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

The link points to a website at NCEDC to download the full moment tensors inversion software The moment tensor analysis conducted in the current project is based on the full moment tensor model described in Minson and Dreger (2008). The software including source, examples and tutorial can be obtained from ftp://ncedc.org/outgoing/dreger (download file pasi-nov282012.tar.gz). Performance criteria, mathematics and test results are provided by Minson and Dreger (2008), Ford et al. (2008, 2009, 2010, 2012) and Saikia (1994). References: Ford, S., D. Dreger and W. Walter (2008). Source Characterization of the August 6, 2007 Crandall Canyon Mine Seismic Event in Central Utah, Seism. Res. Lett., 79, 637-644. Ford, S. R., D. S. Dreger and W. R. Walter (2009). Identifying isotropic events using a regional moment tensor inversion, J. Geophys. Res., 114, B01306, doi:10.1029/2008JB005743. Ford, S. R., D. S. Dreger and W. R. Walter (2010). Network sensitivity solutions for regional moment tensor inversions, Bull. Seism. Soc. Am., 100, p. 1962-1970. Ford, S. R., W. R. Walter, and D. S. Dreger (2012). Event discrimination using regional moment 665 tensors with teleseismic-P constraints, Bull. Seism. Soc. Am. 102, 867-872. Minson, S. and D. Dreger (2008), Stable Inversions for Complete Moment Tensors, Geophys. J. Int., 174, 585-592. Saikia, C.K. (1994), Modified Frequency-Wavenumber Algorithm for Regional Seismograms using Filons Quadrature: Modeling of Lg Waves in Eastern North America. Geophys. J. Int., 118, 142-158.

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

The Renewable Electricity Procurement Options Data (RE-POD) is an aggregated dataset meant to help local jurisdictions and utility customers within those jurisdictions understand the options that may be available to them to procure renewable electricity or renewable energy credits to meet energy goals. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.