The German software company Pvradar Labs has issued a Python programming package for owners and engineers of solar assets who want to build location-specific models.
German software company Pvradar Labs has released a Python programming toolbox for practical people in the industry that are building site-specific models. The package offers a shortcut to adjust the yield models with the choice of users from a wide range of temperature, rainfall, pollution and snowfall datasets.
The company says that it makes a shift possible from trusting on general assumptions to more accurate simulations.
“The Pvradar Python package was built to close the gap between academic research and practical application, making accurate modeling accessible to everyone,” Franco Clandestino, product of product at Pvradar, said told PV Magazine.
Many performance -engineers responsible for PV system models have used general assumptions to predict PV system losses as a result of factors such as temperature, rainfall, pollution, snowfall and tracker disruptions, according to Clandestino.
“Although Black-Box Tools can achieve good accuracy in some cases, they do not necessarily offer control or visibility about what is happening behind the scenes, such as which models are used, with which parameters and under what assumptions,” Clandestino explained.
He added as an example that applying a fixed 2% pollution loss assumption “completely wrong” in dry regions. Similarly, the effects of snow, cell temperature and radiation can vary, depending on the chosen model.
Ideally, location -specific models must be used with parameters calibrated based on measured Site data. “Complete transparency is essential to build trust and to get reliable results that support the solid economic planning,” says Clandestino.
The new product comprises pre-built functions, which reduces the need to format imported PV-related data. “With a single line code you can access several synchronized databases, a wide range of models from the latest research, for example from Pvlib. And validate and validate your own models,” said Clandestino, referring to the PVLIB open-source software.
“Everyone who has worked with data knows how time-consuming it can be to deal with time zone mism matches, unit conversions, time stamp lines, inconsistent formats or gap fulfilling. These challenges can be a nightmare that introduce silent errors that can lead to bad decisions,” he added.
Global satellite and land-based environmental sources are available. Internal data sets are supported, as well as external historical weather, radiation and satellite datas sets, including snowfall, snow depth, snow density and other meteorological data, such as the ERA5 and Merra-2 data sets.
Pvradar, founded in 2022, develops software for large-scale solar PV analysis, including tools to understand climate and weather risks, make estimates and to optimize cleaning strategies.
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