Inspiration

This project is inspired by research that uncovered the disconnect between the local lending community and the solar PV business. Solar is growing, but local lending is not. We aim to bridge that gap.

What it does

Symsolar uses data analytics to drive customer identification and acquisition for residential rooftop and community solar.

How I built it

We used current PV installation data and an existing neural networks package to build a model that provides capacity values for RRSPV at any given address in Boston.

Challenges I ran into

We were challenged by lack of data on electricity consumption. So, we 'hacked' it and used other publicly available sources of data to build an estimation model for home by home solar capacity for any address in the US.

Accomplishments that I'm proud of

We're proud of being able to develop a model in such little time and course-correcting when we ran into the data availability problem.

What I learned

We learned that finding data is a real problem, but it also presents a multi-million dollar opportunity.

What's next for Symsolar

Connecting with local lenders, installers, and community organizations to validate the business model and continuing to fine tune the parameters for our core algorithm.

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