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New generation Solargis Evaluate: data, PV design & simulation, analysis, and reports in one cloud-based solution. Discover more ->

We’ve just added another layer of precision to Solargis Evaluate. Following the recent launch of snow loss calculations, soiling losses are now also part of our PV simulation toolkit – helping developers, investors, and technical advisors model real-world PV performance more accurately.

The new Solargis Evaluate, launched earlier this year, now allows users to automatically simulate soiling losses using the Solargis soiling loss model. This helps PV developers and operators plan solar panel maintenance more effectively and mitigate financial losses caused by soiling.

Why calculating soiling losses matters?

Soiling occurs when dust, sand, pollen, pollution, or other particles accumulate on PV modules, reducing the amount of solar radiation that reaches the cells and thereby lowering energy production. In some regions, soiling can cause substantial energy losses if not properly monitored and managed.

Understanding soiling losses is critical especially in arid and semi-arid locations, but also in areas with high agricultural or industrial activity. As these losses directly impact PV output, accounting for them is key to realistic performance estimates and better-informed decisions.

Soiling rate (1)

How does Solargis Evaluate calculate soiling losses?

The Solargis soiling model consists of two main components:

  • Accumulation of particles – The model calculates the mass (m) of suspended particles deposited on PV surfaces, considering both natural and scheduled cleaning events.
  • Soling ratio calculation – Once the accumulated mass (m) is determined, an empirical relationship estimates the soiling ratio.

The Solargis soiling model integrates data from two leading sources:

  • Meteorological parameters including temperature, atmospheric pressure, wind speed, and rainfall are sourced from ERA5 (31 km resolution) and ERA5-Land (9 km resolution).
  • Particulate matter parameters (PM2.5 and PM10) are derived through harmonized data from multiple sources, based on CAMS reanalysis (80 km resolution, 3-hour intervals). This coarser resolution may reduce local accuracy.

When calculating soiling losses, Solargis Evaluate users can now choose from three options:

  • Soiling loss simulation calculated by the Solargis soiling loss model
  • Option to manually configure monthly soiling loss values if you have your own assumptions based on site knowledge, cleaning schedules, or available field data
  • Default setting – flat soiling losses value (3%)

Soiling losses

soiling graph 3

Once defined, the losses are factored directly into the energy yield calculations, providing a more accurate PVOUT (net AC output) and giving you a better understanding of how soiling affects your project’s performance over time.

This functionality is available in the Energy System Designer within Solargis Evaluate, where soiling losses can be applied on a monthly basis.

Why soiling losses matter for bankability

Incorporating soiling losses into energy yield simulations ensures that financial models reflect the true variability of PV performance. It helps you:

  • Avoid overestimation of annual energy yield
  • Better reflect cleaning strategy and maintenance planning
  • Improve accuracy in energy assessments submitted for due diligence

As with snow losses, our aim is to equip you with solutions that reflect the realities of PV operations in different environments and climatic zones.

Solargis Evaluate offers the most precise PV simulation on the market

Solargis Evaluate is designed to bring a new level of precision to PV project evaluation. Leveraging 15-minute time series data, proprietary albedo data, ray tracing technology for bifacial PV simulations, and now snow and soiling loss modeling, you can capture more variables that affect PV performance – all in one cloud-based platform.

Want to learn more about how to apply soiling loss assumptions in your project?

Learn more about the Solargis soiling loss model here.

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