Try the Solcast API
Solcast takes on the many challenges of producing live and forecast solar data, so that you don’t have to. That means making the data easy to access, validate and integrate. We provide instant access to live and forecast data products via this web interface, which is free to try. These include direct estimates of global, direct and diffuse solar radiation, as well as PV power output.
Commonly Asked Questions about Advanced PV Power Model
Developing a PV model involves gathering detailed specifications of the PV system and weather data. This data is used to create a simulation using tools like pvlib and validating the model by comparing its output with historical production data. Continuous validation and refinement of the model based on performance feedback are essential to maintain reliable solar power output predictions.
Weather significantly impacts PV production, so PV forecast accuracy is a function of both weather forecast accuracy and the PV power model you are using. An accurate, high resolution irradiance and weather forecast (including cloud opacity, temperature, and atmospheric conditions) requires a good PV power model to produce and accurate PV power forecast. The Solcast API takes these factors into account to deliver high-resolution weather and solar forecasts required for precise PV performance predictions.
“Tuning” is important because it uses historical power production data to adjust and refine the model, addressing any discrepancies between the model’s predictions and actual performance. This process is especially valuable in improving forecast accuracy for complex sites or when detailed site data is not available. By tuning the model empirically, you’re able to create a model that is accurate enough to meet your operational needs, without the full detailed site data audit you might otherwise require.
Site measurements, such as power output data, enhance forecast accuracy by enabling empirical improvements to the PV power model. This data allows the model to be fine-tuned, reducing errors and aligning predictions more closely with real weather and solar conditions.
When assessing the accuracy of a given model, it’s important to consider that both the input data (i.e. irradiance parameters) and the power model are sources of bias and error. To learn how you can minimise accuracy gaps, check out the step-by-step guide on how to run your own assessment and assess quality or you can check for a guided evaluation with the team.