quantify-snow-pvlib-marion-townsend

Quantify snow losses using pvlib’s Marion and Townsend models¶

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

We will:

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

The example uses pvlib’s Marion and Townsend models, both of which are available via convenient wrappers in the PVRADAR SDK.

In [1]:

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

Set up the location and plant design¶

In [2]:

location = 59.922, 17.578  # Uppsala, Sweden
site = PvradarSite(location=location, interval='2019-06-01..2021-05-31')

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

In [3]:

site.design = make_fixed_design(tilt=20, clearance=0.8)

Run the snow model¶

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

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

In [4]:

# Import wrappers aroung pvlib models
from pvradar.sdk.pv.irradiance.losses.snow import pvlib_snow_loss_marion, pvlib_snow_loss_townsend

# Select the snow model
my_snow_model = pvlib_snow_loss_marion
# my_snow_model = pvlib_snow_loss_townsend  # Uncomment to try Townsend model

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

In [5]:

# set preferred snowfall and air temperature datasource
site.hooks(for_resource(R.snowfall).prefer_datasource('era5'));

In [6]:

# Run the model
snow_loss_factor = site.run(my_snow_model)         # Try using townsend model instead!

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_snow_model).display_flowchart()

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Plot snow 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]:

snowfall = site.resource(R.snowfall(to_unit='cm', to_freq='D'))
air_temp = site.resource(R.air_temperature)
poa = site.resource(R.global_poa_on_front)

In [9]:

resource_plot(snow_loss_factor, snowfall, poa)

Compare energy yield with and without snow¶

In [10]:

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

# Energy with snow losses
with site.hooks(for_resource(R.snow_loss_factor).use_model(my_snow_model)):
    energy_with_snow = site.resource(R.grid_energy(to_unit='MWh', to_freq='D'), label='with snow')

# Plot both
resource_plot(energy_without_snow, energy_with_snow)

Summary: How much energy is lost to snow?¶

In [11]:

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

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

The average snow loss factor is: 3.11%
The share of energy actually lost is: 0.49%

¶

Conclusion: Why use PVRADAR for snow loss modeling?¶

With just a few lines of code, you built a fully functional snow 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 snow:

Combine snow, soiling, 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 snow 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!