SunCast - Solar Power Nowcasting

We take satellite images and predict short-term - 3 hours ahead - change
in solar shortwave radiation. This helps to use solar power more efficiently,
balancing supply and demand and reducing spinning reserves.

See our demoLearn more

Solar power is unpredictable

Balancing power production and consumption is key when working with solar power. If production exceeds demand, energy storage becomes an issue. If production falls short, consumers face black-outs. As the price of capturing solar energy goes down, predicting solar irradiance has become key to increasing efficiency of power grids and to minimize the use of fossil fuels on spinning reserves.

Downward Shortwave Radiation

This map shows the continental United States and its actual and predicted shortwave irradiation at selected times.

Color represents Downward Shortwave Radiation: 0 1500 W/m2

Time (UTC) DSR (W/m2)


SunCast uses satellite image data from NOAA, collected by the GOES satellite system and made available every hour for the continental United States. This data is coming to us in NetCDF format and is already enriched with Downward Shortware Radiation information by NOAA.

NOAA's various output formats

Deep Learning

Our Deep Learning Architecture

We built our solution on two fundamental deep learning technologies: convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) neural networks. Combined CNN-LSTM architectures are specifically designed for sequence prediction problems with spatial inputs, like images or videos.


We provide fast APIs to access our predictions for any lat/long in the United States. Our API returns shortwave radiation predictions for 3 hours, along with last 3 hours of actual data. An example GET call and JSON response can be seen here.

Request for San Francisco:



{"latitude":37, "longitude":-122, "predicted_at": 2021-07-26T07:00:00.000Z, "last_3_hours":[461,295,166], "next_3_hours":[35,2,5]}}

The Team

Dhileeban Kumaresan

Data Science Graduate Student @ UC Berkeley

Ernesto Martinez

Data Science Graduate Student @ UC Berkeley

Richard Wang

Actuarial Manager @ Lyft

Richard Cziva

Data Engineer @ Berkeley Lab