Current approaches for selecting solar datasets

There is not yet a standardized approach for identifying the best solar and meteorological dataset.

Two approaches commonly used by technical advisors to justify estimates of solar resource are:

1. Weighted average approach

This approach relies on taking a?weighted mean of monthly averages of solar resource and air temperature data from multiple sources. Weights to different datasets are assigned based on parameters such as spatial resolution and temporal coverage.

The main argument for use of this method is that the weighted mean should give a resource estimate that has lower uncertainty than that of?a single dataset.

2. Median approach

Here, long-term monthly and yearly values of multiple data sources are compared. If?any data source shows inconsistency relative to other datasets, it is identified as an outlier and is removed from further analysis.

From the remaining datasets, the dataset with the median GHI value is selected as input for yield estimation.

Whilst, at first glance, these approaches might seem more reliable than relying on a single data source, both have limitations.

The weighted mean approach requires generation of synthetic hourly data from monthly averages. Using synthetically generated typical year dataset creates multiple limitations for evaluation of a solar project, as summarised in table:

This approach is subjective and results in a dataset that can always be disputed by stakeholders in the project evaluation process. Use of this approach reduces both transparency and efficiency in the evaluation of solar projects.

The second approach, which relies on selection of the dataset with the median GHI value after discarding outliers, can only be considered more effective if the datasets being compared have similar qualities – i.e. they can be validated, are available as high resolution time series, and have long temporal coverage, including the recent period.

If the dataset with the median value is available only as a Typical Meteorological Year (TMY) dataset, this data cannot be validated against on-site measurements.

For this reason alone, use of datasets available only as typical year dataset is not justified.

Moreover, it might be difficult to select a dataset that is the median value for multiple parameters influencing the performance of a PV system – including GHI, DNI, and TEMP.

To help solar industry professionals choose the right dataset for evaluation of solar projects, we put together so-called MASTER approach. You can learn more about it here: https://lnkd.in/eyueXZqW