It is standard practice to measure solar radiation on a project site for performance monitoring of large PV power plants. When possible, solar radiation is also measured in the development phase with the objective of reducing uncertainty of long-term modeled time series. One of the key challenges faced by users of measured solar irradiance data is high occurrence of suspicious values.
Before the measurements are used for performance assessment or for adaptation of modeled time series, all questionable values in the measured time series need to be identified and flagged via quality control procedures. Quality control is a tedious and time consuming process if you don’t have the right tools and longterm experience. Our experience of working with measurements from hundreds of locations globally has helped us to develop a streamlined methods and unique capabilities. Benefit from our data quality control service.
The magnitude of errors because of cosine effect, temperature response, spectral sensitivity, stability, non-linearity, etc. depends on quality of sensors and local conditions. We review technical specification of sensors and calibration certificates as first step in estimating uncertainty of the measured data.
Installation related errors such as misalignment of sensors, shading by surrounding objects, etc. are identified, and the affected measurements are flagged
Soiling of sensors (because of dust, snow, water droplets, frost, bird droppings, etc.) is difficult to prevent in most cases. Irregularities because of soiling are identified by detailed visual inspection of the data
All data values are flagged according to results of quality control tests. Missing values are gap-filled, if required, with Solargis time series data
Statement of uncertainty are provided for the quality controlled datasets enabling use of data for bankable performance assessment and site-adaptation of satellite-modeled historical time series
