Methodology for Climate Central’s WeatherPowerTM (Wind and Solar Electricity Forecaster), version 3.0

The report describes the technical framework of WeatherPower v3.0, which uses a 0.05° grid of weather data (wind speed and solar irradiance) and installed capacity data to estimate electricity generation for 'Yesterday', 'Today', and 'Tomorrow'. It outlines the data sources for utility and residential solar and wind capacities, the mathematical power curves used to calculate generation, and the reporting metrics used to contextualize the results, such as CO2 emissions avoided and equivalent homes powered. The document also includes a validation study comparing its wind generation estimates against actual data from the Electric Reliability Council of Texas (ERCOT).

• WeatherPower v3.0 estimates solar and wind electricity generation for the continental U.S., Alaska, and Hawaii using a uniform 0.05° latitude by 0.05° longitude grid of weather data.
• The tool's installed capacity database grew between version 1.0 and 3.0, with nationwide solar-PV capacity increasing by 55% (from 54,644 to 84,648 MWDC) and wind capacity reaching 107,968 MW, a 12% increase over the previous year.
• Residential solar capacity is estimated by combining data from Lawrence Berkeley National Laboratory’s Tracking the Sun and Google’s Project Sunroof, with final state-level adjustments made using Solar Energy Industry Association (SEIA) data to address underestimations.
• Electricity generation is calculated using specific power curves: residential solar is correlated with global horizontal irradiance (GHI), utility-scale solar uses Plane of Array (POA) irradiance, and wind turbines use a composite facility-scale curve based on wind speed.
• The tool provides several comparative metrics to contextualize energy generation, including the number of smartphones that could be charged, car miles driven, and trees planted based on avoided CO2 emissions.
• A validation study in the ERCOT region showed that WeatherPower's daily wind generation estimates are on average about 10% higher than reported data, with a correlation coefficient squared (R2) of 0.87 for the full region.

APA

Brady, J., Chu, C., Colter, E., Fielding, J., Hadj-Chikh, L., Larson, E., Mimi, Y., Singh, J., Weber, K., & Zecchin, K. (2020). Methodology for Climate Central’s WeatherPowerTM (Wind and Solar Electricity Forecaster), version 3.0. Climate Central. https://wp-static-prod.s3.amazonaws.com/WeatherPower_v3.0_Methodology_29Sep2020.pdf

Chicago

Brady, Jennifer, Christopher Chu, Elise Colter, Jessica Fielding, Leila Hadj-Chikh, Eric Larson, Yazan Mimi, et al. Methodology for Climate Central’s WeatherPowerTM (Wind and Solar Electricity Forecaster), version 3.0. Climate Central, 2020. https://wp-static-prod.s3.amazonaws.com/WeatherPower_v3.0_Methodology_29Sep2020.pdf.

Wikipedia

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BibTeX

@techreport{brady2020methodology, 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’s WeatherPowerTM (Wind and Solar Electricity Forecaster), version 3.0}}, institution = {Climate Central}, year = {2020}, month = sep, url = {https://wp-static-prod.s3.amazonaws.com/WeatherPower_v3.0_Methodology_29Sep2020.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }