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# Time-series Data

SolarAnywhere® time-series data is available for historical, real-time and forecasted time periods with a SolarAnywhere Sites, Forecast or SystemCheck® license.

Historical time-series data can be downloaded from the SolarAnywhere data website or requested via API with access to a Sites license. This data can be requested in 15-minute, 30-minute or hourly resolution. The period available and the spatial resolution are dependent upon the geographic data region.

Historical time-series data is also recommended for calculating PV system losses. Using time-series data in snow-loss modeling can help reduce the occurrence of over or underestimation of losses due to snow cover on PV panels. Historical time-series data is the most bankable data for PV project financing and asset management. It’s used to determine the interannual variability of the solar resource, as well as expected irradiance totals at various probability of exceedance levels. Lastly, SolarAnywhere historical time-series data can be tuned to high-quality, ground-measured data to further reduce resource uncertainty. To learn more, visit the ground tuning services page.

Users can demo time-series data with SolarAnywhere Public.

### Calculating Interannual Variability

Interannual variability can be calculated as the coefficient of variation. The coefficient of variation is the ratio of standard deviation $(σ^{AI})$ of annual irradiances $(\overline{x^{AI}})$ for the full SolarAnywhere time-series dataset divided by the mean of annual irradiances (as shown in the equations below).

$$\overline{x^{AI}}=\frac{∑_{i=1998}^{n}x_{i}^{AI}}{N}$$

$$σ^{AI}=\sqrt{\frac{∑_{i=1998}^{n}{(x_{i}^{AI}-\overline{x{^{AI}}}})^2}{N}}$$

$$CV=\frac{σ^{AI}}{\overline{x^{AI} }}*100$$

Where $N$ represents the number of full years in the SolarAnywhere time-series dataset, and $x_i{^{AI}}$ represents the annual total irradiance from each year in the full SolarAnywhere dataset, and $n$ represents the last full year of data available in the full SolarAnywhere time-series dataset.

### References

1 SolarAnywhere versions 3.4 and prior: At midnight UTC on the 16th of each month, SolarAnywhere data from 1 month prior is re-generated using the SolarAnywhere historical model and archived. Once data is archived, it will not change within the data version. For example, at midnight UTC on the 16th of July, the data for June will be re-generated and archived with historical models.

SolarAnywhere versions 3.5 and later: At midnight UTC on the 6th of each month, SolarAnywhere data from 2 months prior is re-generated using the SolarAnywhere historical model and archived. Once data is archived, it will not change within the data version. For example, at midnight UTC on the 6th of July, the data for May will be re-generated and archived with historical models.

Utilize the Irradiance Observation Type irradiance and weather data field in the SolarAnywhere output format or request it in your API requests to understand when this transition from the real-time model to the historical model occurs in your data.