Developing a data analysis tool to model and forecast community solar adoption as well as other key renewable technologies
Team members: Catalina Perez-Aguirre, Ellen Reinhard, Nile Berry and Paras Sethi
Solar panel efficiency has never been higher than it is today, while simultaneously, production costs have never been lower. However, there remains an information gap at the community level to fully grasp and forecast the benefits of implementing solar panel technology. The inability to predict solar on a macro scale limits many lawmakers to creating a hyper-local policy that could incentivize broad-based solar adoption.
When compared to other renewable energy technologies, solar energy production is unique. The physical placement of solar panels is directly correlated with the technology's overall efficacy. Neighboring building shadows, solar panel angle, and specific geographic location all play a key role in determining the technology's overall energy output and success.
Our research aims to close this information gap by creating a data model (using publicly available resources) to accurately and effectively forecast various solar adoption scenarios across a community. Our model focuses specifically on Kendall Square in Cambridge, Massachusetts, but could easily be adapted to another neighborhood of similar size.
Our goal is to provide policymakers with a simple forecasting tool that provides specific insights for their community to better understand the highest and best use of solar installation today and explore possible future scenarios. Beyond this, we incorporate additional calculations for other leading renewable energy technologies (geothermal, nuclear, etc.) to provide users with a broad understanding of how these technologies might factor in as well.
We hope that this research will provide community stakeholders with a key resource to better understand solar and other renewable technologies and empower them to think through adoption strategies that would lead to wide-scale decarbonization.