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Solar Electricity Nowcasting

All Contributors

This is a "meta-repository" for Open Climate Fix's solar electricity nowcasting project. See this great Wired article about OCF's solar electricity forecasting work for a good intro to solar electricity nowcasting.

The plan is to enable the community to build the world's best near-term forecasting system for solar electricity generation, and then let anyone use it! :) We'll do this by using state-of-the-art machine learning and 5-minutely satellite imagery to predict the movement of clouds over the next few hours, and then use this to predict solar electricity generation.

The term "nowcasting" just means "forecasting for the next few hours using statistical techniques".

Why is all this stuff open-source?

In OCF, we're curious to see if it's possible to rapidly mitigate climate change by:

  1. Enabling thousands of people to help solve ML problems which, if solved, might help reduce CO2 emissions
  2. Running small(ish) pilot projects to implement the best solution in industry
  3. Enabling thousands of practitioners to use the code in their products.

What's the likely climate impact?

It's really, really, really hard to estimate climate impact of forecasting! But, as a super-rough back-of-the-envelope calculation, we estimate that better solar forecasts, if rolled out globally, could reduce CO2 emissions by about a billion tonnes between now and 2035.

Getting involved

Overview of OCF's nowcasting repositories

Downloading data & getting the data in the right shape for ML experiments

  • nowcasting_dataset: Pre-prepares ML training batches. Loads satellite data, numerical weather predictions, solar PV power generation timeseries, and other datasets. Outputs pre-prepared ML training batches as NetCDF files (one batch per NetCDF file).
  • Satip: Retrieve, transform and store EUMETSAT data.
  • pvoutput: Python code for downloading PV data from

Older code (no longer maintained)

Machine Learning

Main repositories for our experiments:

  • satflow: Satellite Optical Flow with machine learning models. Predicting the next few hours of satellite imagery from the recent history of satellite imagery (and other data sources).
  • predict_pv_yield: Using optical flow (and the output of satflow) & machine learning to predict solar PV yield (i.e. to predict the power generated by solar electricity systems over the next few hours). An older set of experiments is in predict_pv_yield_OLD, which is no longer maintained..
  • nowcasting_utils: Forecasting performance metrics, plotting functions, loss functions, etc.
  • nowcasting_dataloader: PyTorch dataloader for taking pre-prepared batches from nowcasting-dataset and getting them into our models.

PyTorch implementations of ML models from the literature

Older code (no longer maintained)

Operational solar nowcasting

  • nowcasting_api: API for hosting nowcasting solar predictions. Will just return 'dummy numbers' until about mid-2022!

For a complete list of all of OCF's repositories tagged with "nowcasting", see this link

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Damien Tanner










Peter Dudfield




rachel tipton

👀 💻

This project follows the all-contributors specification. Contributions of any kind welcome!