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Energy yield simulation
Medium and large-scale ground-based projects are sensitive to every percentage point of uncertainty.
Higher uncertainty reflects in higher risk and less favorable financing terms.
We combine Solargis satellite-based data with your on-site measurements to reduce the uncertainty of the estimated energy output and achieve more accurate financial estimates.
If you have high-quality on-site measurements of irradiation for a period of 9-12 months, we can adapt our satellite data to improve the overall bias and fit of statistical characteristics.
Our Site Adaptation service will give you locally-enhanced solar and meteo parameters.
This way, you can reduce uncertainty of power plant design and energy yield simulations to a minimum.
Sensors on the ground are prone to errors – making the process of data evaluation challenging.
We take the ground-based measurements and run a suite of procedures to identify errors, validate the measurements, and check for missing data.
Only inputs that pass our quality assurance tests are used in the Site Adaptation service to ensure the process is not impacted by poor-quality data.
In the Site Adaptation process we use model inputs that better represent the local conditions and recalculate the data.
This gives us results that have lower uncertainty, both for the full 30-year history of the site and for the future.
As a result, we deliver higher-accuracy data with features adapted to local conditions, reducing both systematic and random errors. The delivered data is also internally better harmonized (with better quality and structure).
With 20+ years of experience, 2000+ projects, and full control of our proprietary models, with the Site Adaptation service we are able to deliver data of incomparable quality.
The site adaptation process helps fix issues in satellite-derived data, such as under/over-estimation of local aerosol loads.
This is especially true when the magnitude of this deviation does not vary over time or has a seasonal periodicity.
We can adapt satellite-derived DNI and GHI, air temperature, and other meteorological parameters to the local climate conditions, which cannot be captured in the original satellite and atmospheric inputs.
Based on the availability of ground measurements and using our post-processing algorithms, we validate and improve the accuracy of parameters such as air temperature, wind, relative humidity, precipitation, albedo, and others.
This gives us results that have lower uncertainty, both for the full history of the past 30 years of time series data and for the future.
