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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.

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Please use the latest version of pvradar-sdk

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📡 Data Access & Validation

Retrieve, compare, and validate environmental and operational data from public and private sources.

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Access meteorological data (ERA5, MERRA-2)
Learn how to get precipitation, temperature, wind speed, and more in a single line of code.
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Access snowfall, snow depth, and snow density
Retrieve and compare snowfall metrics relevant to snow loss modeling.
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Validate rainfall data from NOAA vs satellite sources
Benchmark ground station data against satellite estimates to select your preferred data source.
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☀️ Model PV Performance

Build model chains and quantify the physical effects that influence PV system performance.

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Quantify soiling with pvlib (Kimber & HSU models)
Apply pvlib-based soiling models with custom parameters and compare outputs.
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Quantify snow losses with pvlib (Marion & Townsend models)
Simulate snow-related production losses using physical and empirical models.
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Quantify tracking losses due to stuck trackers
Estimate energy loss from tracking system failures based on irradiance and geometry.
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📈 Model Calibration & Validation

Use measurements to calibrate model parameters and test how accurately models reproduce observed behavior.

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Fit PVSyst-like temperature coefficients
Derive u_c and u_v temperature coefficients from measured module temperatures.
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Fit HSU soiling model parameters against measured data
Estimate monthly soiling loss factors and quantify energy losses using HSU soiling model with parameters optimized against actual measurements in similar conditions.
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📡 Yield Forecasting & Optimization

Use models and forecasts to support operational decisions and estimate future performance.

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Solar Power Yield forecast using hefty-PVRADAR integration
Retrieve irradiance forecasts from the ECMWF IFS via the hefty Python package and turn them into yield predictions for your solar asset with PVRADAR.
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🧪 Research & Case Studies

Explore research applications, scientific analyses, and examples developed together with external experts and partners.

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Analysing Aerosol Dynamics in Doha and Beijing - Dr. Yazan J.K. Musleh
Analyze aerosol dynamics and characterize airborne dust using AOD and Ångström Exponent from MERRA-2.
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