Our mission is to make your job simpler. Part of this includes producing the most delicate data, making it quick and simple to obtain, and providing you with bonus rewards to get you going. However, we aim to make it as easy as possible for you to comprehend how we compare to other solar data suppliers.
Streamlined Data Distribution
Suppose we wish to define the bias in particular for locations other than the validation sites. In that case, we may use the simplified assumption of normality of variances between the simulation and the observed values for model estimations. For practical usage, statistical measurements of precision have to be transformed to represent the natural variability of possible inaccuracy better.
Users should not anticipate satellite-based solar simulations to have 0% uncertainty, just like any other measurement method. Nonetheless, suppose the physics depicted by the algorithms are put into practice. In that case, the model should behave stably and uniformly for the geographical circumstances for which it has been measured and verified.
Representation of Accuracy Indicators
Validation statistics, which are computed for each site, describe the efficiency of satellite-based modeling. Traditionally, bias is regarded as the initial sign of model accuracy; yet, model accuracy should be interpreted after considering all measurements.
While bias is useful for understanding a probable inaccuracy in a long-term prediction, MAD and RMSD are useful for determining the reliability of energy modeling and operational estimations. Validation statistics are often standardized and presented as a percentage.
Uncertainty Ranges Estimated for Generation Solar Data Model
Examining the distribution of bias across many geographic areas and scenarios led us to the finding that in most cases, the predicted bias for yearly statistics is 0% for GHI values and 2% for DNI values. High latitudes, areas with humid tropical climates, and places with rapidly changing atmospheric aerosols are examples of situations where the predicted bias can be higher.
Relying on the verification of Generation Solar Data information, a case-by-case probability estimation can be obtained by examining the performance of the model while analyzing local meteorological and geographic characteristics. Areas with a limitation of reliable ground measurements may have greater bias numbers.
Validation of Generation Solar Data Albedo Data
A comparative analysis of our calculations and ground measurements of albedo was done to evaluate the accuracy of Generation Solar Data albedo data products. It is crucial to note that an albedometer’s range of vision is often narrower than the range specified by an image of aerial photographs.
When we check at satellite images of the region around the albedo meters, we can observe that the topography is not always uniform. The disparity in regions covered by the FOV of albedometers and the satellite image consequently affects the assessment of the precision of our albedo products. An expansion of the validation research is advisable to validate and support these findings.