Photovoltaic (PV) systems, particularly those installed in regions prone to soiling such as arid areas, coastal sites, and agricultural zones, can experience energy losses of up to 20–30% annually. These losses translate to financial impacts exceeding €10 billion in 2023.
This thesis aims to develop a robust and comprehensive method to predict soiling accumulation on PV modules and systems by combining real-world environmental modeling with operational PV data (electrical, thermal, optical). The research will follow a bottom-up approach in three stages:

1. Component/Module Level: Reproduction and modeling of soiling accumulation in laboratory conditions, followed by experimental validation. This stage will leverage the CEA’s expertise in degradation modeling, including accelerated testing.

2. Module/System Level: Implementation of monitoring campaigns to collect meteorological, operational, and imaging data, combined with field soiling tests on a pilot site. The data will validate and enhance CEA diagnostic tools by introducing innovative features such as AI-driven soiling propagation prediction.

3. System/Operational Level: Validation of the proposed method on commercial PV modules in utility-scale PV plants, aiming to demonstrate scalability and real-world applicability.

The outcomes of this thesis will contribute to the development of an innovative tool/method for comprehensive soiling diagnostics and prognostics in PV installations, enabling the minimization of energy losses while anticipating and optimizing cleaning strategies for PV plants.