quantify-soiling-pvlib-kimber-hsu

Quantify soiling losses using pvlib’s HSU and Kimber models¶

In this example, we will show how to quantify soiling-related energy losses for a solar plant using the PVRADAR SDK.

We will:

Create a site and define its design
Run a soiling loss model
Visualize inputs and soiling losses
Compare energy yield with and without soiling

In [1]:

from pvradar.sdk import PvradarSite, R, resource_plot, for_resource, make_tracker_design

Set up the location and plant design¶

In [2]:

location = 36.740, -4.442  # Malaga, Spain
site = PvradarSite(location=location, interval='2018..2020')

In this example we assume a HSAT tracker design but you can also choose to define a fixed-tilt design.

In [3]:

site.design = make_tracker_design(max_tracking_angle=60, night_stow_angle=20, dc_ac_ratio=1.0)

Run the soiling model¶

The PVRADAR SDK provides wrappers around common soiling models from pvlib.

We will start with the HSU model, but you can also try the Kimber model by switching one line. You can also create your own soiling model (or any other type of model) completely from scratch.

In [4]:

# Import wrappers aroung pvlib models
from pvradar.sdk.pv.irradiance.losses.soiling import pvlib_soiling_hsu, pvlib_soiling_kimber

# Select the soiling model
my_soiling_model = pvlib_soiling_hsu
# my_soiling_model = pvlib_soiling_kimber  # Uncomment to try Kimber model

Optional: define the source of rainfall data for any modeling. You can also integrate your own rainfall measurement.

In [5]:

# set preferred rainfall datasource
site.hooks(for_resource(R.rainfall).prefer_datasource('aemet-grid'));

In [6]:

# Run the model
soiling_loss_factor = site.run(my_soiling_model)

To see the entire model chain that was executed, along with all models and design parameters used, we can display it as a flowchart.

In [7]:

site.profile(my_soiling_model).display_flowchart()

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Plot soiling loss factor and most important inputs¶

Get access to dozens of resource types in a single line of code and using our R-Notation to specify the desired unit, frequency and datasource. Check our Resource DB for a complete list.

In [8]:

rainfall = site.resource(R.rainfall(to_unit='cm', to_freq='D'))
pm2_5 = site.resource(R.pm2_5_volume_concentration(to_unit='g/m^3', to_freq='D'))
pm10 = site.resource(R.pm10_volume_concentration(to_unit='g/m^3', to_freq='D'))

In [9]:

resource_plot(soiling_loss_factor, rainfall, pm2_5, pm10)

Compare energy yield with and without soiling¶

In [10]:

# Energy without soiling losses
energy_without_soiling = site.resource(R.grid_energy(to_unit='MWh', to_freq='D'), label='without soiling')

# Energy with soiling losses
with site.hooks(for_resource(R.soiling_loss_factor).use_model(my_soiling_model)):
    energy_with_soiling = site.resource(R.grid_energy(to_unit='MWh', to_freq='D'), label='with soiling')

# Plot both
resource_plot(energy_without_soiling, energy_with_soiling)

Summary: How much energy is lost to soiling?¶

In [11]:

# Average soiling loss factor (as %)
print(f'The average soiling loss factor is: {soiling_loss_factor.mean()*100:.2f}%')

# Actual share of energy lost (as %)
soiling_energy_loss_fraction = (energy_without_soiling.sum() - energy_with_soiling.sum()) / energy_without_soiling.sum()
print(f'The share of energy actually lost is: {soiling_energy_loss_fraction*100:.2f}%')

The average soiling loss factor is: 1.29%
The share of energy actually lost is: 1.50%

¶

Conclusion: Why use PVRADAR for soiling loss modeling?¶

With just a few lines of code, you built a fully functional soiling loss model, validated it with real-world data, and quantified its impact on energy yield.

But this is just the beginning.

Model and optimize the real-world behavior of your PV plants¶

PVRADAR allows you to model the effect of any technical or environmental factor on plant performance — not just soiling:

Combine soiling, snow, albedo, clipping, battery storage, and more in a single modeling workflow.
Quantify impacts in terms of energy yield, PR, LCOE, and financial KPIs.
Explore what-if scenarios and optimize plant design and O&M strategies.

Build internal tools and advanced applications¶

With PVRADAR, you can turn your models into robust, organization-wide tools:

Build internal web apps that allow colleagues to apply validated models without writing code.
Standardize modeling across teams and projects.
Automate reporting and performance analysis to save time and ensure consistency.

One Python line away from your data¶

The PVRADAR SDK simplifies data access:

Seamlessly connect to satellite data, meteo stations, and internal performance databases.
Standardize and pre-process data for modeling — ready to use in one line of Python.
Keep full control over your data, whether in the cloud or on-premises.

Supported by deep expertise¶

PVRADAR is more than software — it is backed by industry-leading expertise:

Model validation and tuning with high-quality reference data.
Consulting and support on soiling losses, soiling, O&M optimization, and yield forecasting.
Proven track record with leading IPPs, developers, and consultants.

👉 Ready to go further? Contact us to learn how we can help solve your modeling challenge!