Methodology for Climate Central WeatherPower (Wind and Solar Electricity Forecaster)

The document describes the technical framework of WeatherPower v3.4, which uses a 0.05° grid of wind speed and solar irradiance data from MESO to estimate electricity generation. It details the integration of utility-scale and residential solar data (from EIA860, Tracking the Sun, and Project Sunroof) and wind turbine data (from USWTDB). The tool calculates primary metrics like electricity generated and indices (SPI and WPI), as well as comparative metrics such as CO2 emissions avoided and equivalent homes powered. A validation exercise using ERCOT data indicates that wind generation estimates are reasonably accurate, though on average about 10% higher than reported data.

• WeatherPower v3.4 estimates electricity generation for solar-PV and wind-turbine installations across the continental U.S., Alaska, and Hawaii, aggregating results into Nielsen Designated Market Areas (DMA), states, counties, congressional districts, and EPA eGRID regions.
• The tool utilizes a 0.05° latitude by 0.05° longitude grid for weather parameters, sourcing observed and forecast wind speeds at 80-meter elevation and global horizontal irradiance (GHI) from MESO.
• Solar capacity estimates combine utility-scale data from EIA860 with residential data from Lawrence Berkeley National Laboratory’s Tracking the Sun and Google’s Project Sunroof, with final state-level adjustments based on Solar Energy Industry Association (SEIA) data.
• Wind turbine capacity is derived from the US Wind Turbine Database (USWTDB), which for version 3.4 includes data on 63,794 turbines with a total national capacity of 107,968 MW.
• The tool reports several comparative metrics to contextualize energy generation, including the number of smartphones charged, car miles driven, and trees planted, based on the amount of CO2 emissions avoided.
• A validation study comparing WeatherPower estimates with actual wind electricity generation in the Electric Reliability Council of Texas (ERCOT) region showed a correlation coefficient squared (R2) of 0.87 for system-wide daily generation.
• The ERCOT comparison indicated that WeatherPower's daily wind generation estimates are on average approximately 10% higher than the reported data.

APA

Brady, J., Chu, C., Colter, E., Fielding, J., Hadj-Chikh, L., Larson, E., Mimi, Y., Singh, J., Weber, K., & Zecchin, K. (2021). Methodology for Climate Central WeatherPower (Wind and Solar Electricity Forecaster). Climate Central. https://wp-static-prod.s3.amazonaws.com/WeatherPower_v3.4_Methodology_05Aug2021.pdf

Chicago

Brady, Jennifer, Christopher Chu, Elise Colter, Jessica Fielding, Leila Hadj-Chikh, Eric Larson, Yazan Mimi, et al. Methodology for Climate Central WeatherPower (Wind and Solar Electricity Forecaster). Climate Central, 2021. https://wp-static-prod.s3.amazonaws.com/WeatherPower_v3.4_Methodology_05Aug2021.pdf.

Wikipedia

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BibTeX

@techreport{brady2021methodology, author = {Brady, Jennifer and Chu, Christopher and Colter, Elise and Fielding, Jessica and Hadj-Chikh, Leila and Larson, Eric and Mimi, Yazan and Singh, Jaiteg and Weber, Kaitlyn and Zecchin, Kaylee}, title = {{Methodology for Climate Central WeatherPower (Wind and Solar Electricity Forecaster)}}, institution = {Climate Central}, year = {2021}, month = jul, url = {https://wp-static-prod.s3.amazonaws.com/WeatherPower_v3.4_Methodology_05Aug2021.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }