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PV Rooftop Database

The National Renewable Energy Laboratory's (NREL) Photovoltaic (PV) Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The PVRDB data are organized...

Mooney, M. National Renewable Energy Laboratory (NREL)

Jan 01, 2016
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Federal Greenhouse Gas Inventories and Performance

The Federal Energy Management Program (FEMP) provides progress data tables illustrating Federal agency progress in meeting the greenhouse gas (GHG) reduction targets established under Executive Order (E.O.) 13514, as well as the comprehensive greenhouse gas inventories as reported...

Tremper, C. and (EERE), O. Office of Energy Efficiency & Renewable Energy

Nov 25, 2014
1 Resources
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In curation

2024 Standard Scenarios: A U.S. Electricity Sector Outlook

This data corresponds to the 2024 Standard Scenarios report, which contains a suite of forward-looking scenarios of the possible evolution of the U.S. electricity sector through 2050. These files contain modeled projections of the future. Although we strive to capture relevant p...

Gagnon, P. et al National Renewable Energy Laboratory (NREL)

Dec 30, 2024
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Maximum demand charge rates for commercial and industrial electricity tariffs in the United States

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data...

McLaren. . et al National Renewable Energy Laboratory

Sep 19, 2017
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Puerto Rico Solar-for-All: LMI PV Rooftop Technical Potential and Solar Savings Potential

The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level. Each dataset is broken out by inco...

Mooney. . et al National Renewable Energy Laboratory

Jul 28, 2020
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Bhutan Solar Resources

GIS data for Bhutan's direct normal irradiance (DNI), global horizontal irradiance (GHI), and latitude tilt irradiance. Researchers from NREL and the Atmospheric Sciences Research Center (ASRC) at the State University of New York (SUNY) at Albany developed the estimates of Bhutans...

Heimiller, D. et al National Renewable Energy Laboratory

Nov 25, 2014
4 Resources
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Utility-Scale Solar, 2023 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2023 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developm...

Seel, J. et al Lawrence Berkeley National Lab

Sep 05, 2023
5 Resources
1 Stars
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DEPRECATED State Policies and Programs for Community Solar (2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/249 The purpose of this dataset is to summarize current community solar policies and low-income stipulations by the state in the United S...

Xu. . et al National Renewable Energy Laboratory

Apr 05, 2023
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Utility-Scale Solar, 2024 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2024 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developm...

Seel, J. et al Lawrence Berkeley National Lab

Sep 27, 2024
5 Resources
1 Stars
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Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) ...

Ayyanar, R. et al Arizona State University

Feb 01, 2025
12 Resources
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Tracking the Sun

Berkeley Lab's Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed thro...

Barbose, G. and Darghouth, N. Lawrence Berkeley National Laboratory (LBNL)

Oct 01, 2019
6 Resources
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SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS

The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the data...

Palmintier, B. et al National Renewable Energy Laboratory (NREL)

Dec 18, 2020
5 Resources
1 Stars
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Screening Tool for Equitable Adoption and Deployment of Solar (STEADy Solar)

The Screening Tool for Equitable Adoption and DeploYment of Solar (STEADy Solar) is a database and mapping tool designed to promoting clean energy investments for low-income communities across the United States. The tool indicates locations that may be eligible for the Investment ...

Ferrall-Wolf and McLarenNational Renewable Energy Laboratory

May 24, 2024
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NREL GIS Data: Continental United States Photovoltaic (Tilt = Latitude) 10km Resolution (1998 2005)

This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This...

Wood, J. and Laboratory, N. National Renewable Energy Laboratory