Using computers to fix climate change
Open Climate Fix is a non-profit product lab, totally focused on reducing greenhouse gas emissions as rapidly as possible. Every part of the organisation is designed to maximise climate impact, such as our open and collaborative approach, our rapid prototyping, and our attention to finding scalable and practical solutions.
By building out in the open, we can draw upon a much larger pool of expertise than any individual company. We combine existing islands of knowledge and accelerate progress.
We search for Machine Learning problems that will likely have an enormous climate impact if solved. For each of these challenges, we plan to:
- Collate & release data, and write software tools to make it super-easy for people to consume the data
- Run a collaborative “global research project” where everyone from 16-year-olds to PhD students to corporate research labs can help find better ML solutions
- Help to put good ideas into production so that we can start reducing emissions ASAP, and so we can test if the solution really does help in practice.
To date, we have built a community of over 1,000 interested volunteers and collaborated with various researchers from institutions such as University College London, University of Edinburgh, Stanford University, The Alan Turing Institute and the Open Data Institute. We also work closely with industry partners.
Currently we are focused on our solar electricity Nowcasting project. As clouds move over solar panels, the power output moves up and down rapidly. To keep the energy grid in balance, operators need to have readily available power generation reserves which usually come from fossil fuel sources. With better forecasts (short term forecasts are called nowcasts), we will be able to reduce the amount of fossil fuel reserve required. We will now build solar Nowcasting as a product. Our ultimate aim is to reduce annual CO2 emissions by approximately 100 million tonnes globally by 2030. We are engaged in this project with an industry partner with the intention to deploy in a production environment.