CLEO AI, a new ground-breaking tool for solar soiling loss modeling, has been unveiled by a leading international solar company. This cutting-edge technology leverages artificial intelligence (AI) to improve business models and boost energy yield predictions in the solar development sector.

The new model, designed by a highly skilled team, fuses precise local soiling loss measures, comprehensive weather data, and advanced machine learning algorithms. The result is a highly reliable localization of annual soiling losses simulated. Contrasting general approaches that utilize static data, this unique model tailors its estimations based on ever-evolving factors. These encompass aspects like PM2.5 and PM10 concentrations, sulfate and nitrate levels, seasonal variability, and the effects of winds and precipitation.

The organization’s objective is to equip solar production model engineers with advanced tools to constantly enhance performance modeling and maximize returns. The model generates a Typical Meteorological Year (TMY) of daily soiling loss data. This provides granular, location-specific simulations of soiling losses that users can leverage to improve their production models and plan future maintenance routines accordingly.

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The solution significantly improves upon the already extensive network of the company's soiling monitoring stations. These stations are scattered across 27 countries, representing an impressive over 12 GW of installed solar capacity. Moreover, the model continuously evolves in sophistication through machine learning as it learns from an ever-growing dataset. It further integrates new parameters such as module tilt and weather predictions to deliver increasingly precise results.

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Soiling is a location-specific, dynamic challenge that can significantly affect solar asset performance over time. To combat this, the firm's revolutionary CLEO AI-powered model allows customers to take an anticipatory stance and manage this issue effectively. This results in optimized maintenance plans and production forecasts, with the sole objective of achieving the maximum return on their solar investment.

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