Use-Cases & Example Notebooks

Explore real-world use cases with our Python-based framework for solar performance modeling. These Jupyter notebooks demonstrate how to access environmental data, model various types of performance losses, and optimize operational strategies using the PVRADAR Python package.

Please use the latest version of pvradar-sdk if you want to execute the examples on your machine, as we are continuously updating the python package and the example notebooks. Whether you're building internal tools, validating models, or making investment-grade decisions, these notebooks will help you get started.

Start using PVRADAR today - It’s FREE: Installation Instructions

Register for our upcoming webinars or watch recordings of past sessions: Webinars

📡 1. Access to Data

Understand how to retrieve reliable environmental data for any location worldwide. Use satellite and ground-based sources as input to your models or to validate third-party estimates.

• Access meteorological data (ERA5, MERRA-2)

  Learn how to get precipitation, temperature, wind speed, and more in a single line of code.

• Access snowfall, snow depth, and snow density

  Retrieve and compare snowfall metrics relevant to snow loss modeling.

• Validate rainfall data from NOAA vs satellite sources

  Benchmark ground station data against satellite estimates to select your preferred data source.

🌫️ 2. Loss Modeling

Build and run models for different types of performance losses due to environmental effects using validated parameters and time-series input data.

• Quantify soiling with pvlib (Kimber & HSU models)

  Apply pvlib-based soiling models with custom parameters and compare outputs.

• Quantify snow losses with pvlib (Marion & Townsend models)

  Simulate snow-related production losses using physical and empirical models.

• Quantify tracking losses due to stuck trackers

  Estimate energy loss from tracking system failures based on irradiance and geometry.

📈 3. Optimization

Go beyond loss quantification to identify cost-optimal strategies and improve system performance.

• Fit PVSyst-like temperature coefficients

  Derive u_c and u_v temperature coefficients from measured module temperatures.

Can’t find your specific use case? Get in touch!