A desire for open exchange of data/information to improve overall usage and adoption of solar/battery technologies.
What it does
Makes real-time decisions of optimal behavior for controlling solar/battery/grid energy.
How we built it
Using real historical data from commercial buildings, PV generation, and battery behaviors. We integrated these into a webapp built in python.
Challenges we ran into
Scoping, depth of subjects.
Accomplishments that we're proud of
Specing a robust software architecture with naive models in 8hours. Providing a flexible architecture that adapts to variety of assumptions/strategies.
What we learned
The most complicated aspect of these integrations is the Battery management. As was mentioned several times today "the science isn't there yet", so this is a huge difficulty and opportunity for machine learned improvements in behavior.
What's next for CLEAN Coal
Web deployment, more dynamic models, more sophisticated learners, forecasting integration, business model.