Community Solar Developers Look to Ai to Manage Subscribers, Advance Equity
Learn about the challenges community solar projects face with subscribers turning over and how AI can help prevent
Solar Builder Magazine 1:12 pm on June 4, 2024
Machine learning models like gradient boosting classification algorithms, tree-based modeling, and traditional regression techniques are used in solar industry for pattern recognition and relationship understanding. These dynamic approaches improve flexibility over static models. Solstice Energy's AI-driven 'EnergyScore' offers a fair credit assessment tool for community solar projects, aiming to include lower-income participants who often get overlooked by traditional score-based systems but are good bill payers.
- Machine Learning Application: Use of advanced models in the renewable energy sector.
- EnergyScore AI Tool: Solstice Energy's 'EnergyScore' helps identify reliable community solar subscribers irrespective of credit scores, promoting inclusivity.
- Dynamic vs. Static Algorithms: Emphasizing the superiority of machine learning methods over rigid traditional models for risk assessment and customer profiling.
- Financial Inclusion: Efforts to engage low-income individuals in community solar projects, counteracting previous biases in credit score systems.
https://solarbuildermag.com/projects/community-solar-developers-look-to-ai-to-manage-subscribers/