Accelerating the clean energy transition with data, geospatial intelligence and AI
In order to successfully deploy clean energy technologies at scale, it is imperative to consider the many complex legal, financial, technological, and geographical factors which influence project success and failure. At this moment, solar projects fail at staggering rates; Paces estimates that less than 20% of solar projects with interconnection queue positions materialize into operational systems. One of the most critical factors heavily influencing project success happens to be the first step that any solar developer would take – site selection.
Solar developers need a way to find generally suitable locations for solar project development, and the ability to narrow down their search for parcels of land which meet their project-specific requirements. A variety of factors can stop a project in its tracks, such as the presence of nearby wetlands or local resident disapproval; it is therefore imperative to help solar developers curate a quality list of parcels which have high potential to lead to project success.
Yuma is an application built specifically to solve this problem. The platform provides a chat interface which developers can use to query for land parcels in natural language. For example, a developer might input:
“Find parcels with at least 50 MW of ground-mount capacity in Worcester county in Massachusetts”
Under the hood, an AI-driven backend writes the SQL code to query the rich database of parcels meeting the specified requirements. The map view on the right-hand side allows the developer to view the land parcels and explore them visually using satellite imagery. Subsequently, the user can input additional site requirements or make changes to previously specified instructions. The system will regenerate the necessary SQL code and display the updated results to the user.
You can play with the application here, and view the open-source code on github.
