What is the accuracy of Solargis data?

Modified on: Tue, 17 Jan, 2017 at 15:57

The accuracy of a satellite-based model such as Solargis can be characterized by following performance indicators:

1. Bias - characterizes systematic model deviation at a given site.
2. Root Mean Square Deviation (RMSD) and Mean Average Deviation (MAD) - indicate the spread of error for instantaneous values.
3. Correlation coefficient
4. Kolmogorov-Smirnoff index (KSI) - characterizes representativeness of distribution of values

However, for practical purposes and for easy understanding, statistical measures of accuracy are converted into uncertainty, which better characterizes probabilistic nature of a possible error of the model estimate.

The uncertainty of solar resource data is typically quantified for the P90 scenario (use of this confidence interval is considered as standard in solar resource assessment). For example, if we consider that uncertainty at P90 confidence interval is ±5%, it means that about 90% of the time, the true value will fall within ±5% range of the modeled best estimate.

If we consider normal distributions of deviation between modeled and measured values, the standard deviation of bias of values represents 68% probability of occurrence. The uncertainty for the P90 confidence interval can be quantified using the following formula:

Uncertainty at P90 confidence interval = STDEV of biases x 1.2821

P90 uncertainty

The uncertainty of Solargis model based on validation statistics at 189 sites globally is shown below for difference confidence intervals

Solargis model uncertainty

As Solargis data has been validated with only quality controlled data that were measured mostly with secondary standard instruments, we consider the uncertainty of measurements to be ±2% and ±1% for GHI and DNI respectively. Based on this assumption, Solargis user’s uncertainty will be as follows:

Solargis users uncertainty
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