Fracsun has announced the launch of its AI-powered soiling loss modeling tool called CLEO AI. This advanced solution enables the entire solar development sector to fine-tune their production models and improve energy yield assumptions.
Developed by Fracsun’s team of industry experts, the new model integrates precise local ground-based soiling loss measurements, detailed weather data and machine learning algorithms to deliver accurate, localized simulated annual soiling losses. Unlike static, generalized approaches, this dynamic model accounts for evolving factors such as fine particulate matter (PM2.5 and PM10) concentrations, nitrate and sulfate levels, seasonal trends and the impacts of wind and precipitation.
“Our goal is to empower solar production modelers with the tools they need to continuously improve performance modeling and maximize returns,” said Catlin Mattheis, CEO of Fracsun. “By generating a Typical Meteorological Year (TMY) for daily soiling loss data, our model provides granular, site-specific simulations of soiling losses, enabling users to refine their production models and plan future maintenance schedules accordingly.”
Fracsun’s solution builds upon its extensive network of soiling monitoring stations, deployed across 27 countries and representing over 12 GW of installed solar capacity. Through machine learning, the model continuously learns from this growing dataset, incorporating new parameters like module tilt and weather forecasts to deliver increasingly precise results.
“Soiling is a dynamic, site-specific challenge that can significantly impact solar asset performance over time,” Mattheis continued. “With the CLEO AI-powered model, our customers can now proactively manage this issue, optimizing maintenance plans and production forecasts to maximize their return on investment.”
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