What is this about?
Comparing measurement data against a trusted reference is one of the most effective ways to assess data quality. The concept is simple: datasets representing the same irradiance at the same location should show a strong level of agreement. By visualising and quantifying the differences between measured and reference data, EKO Q helps evaluate data consistency, identify anomalies, and highlight recognizable patterns that may require further investigation.
Why it matters
When measurement data shows strong agreement with the selected reference, it increases confidence in both the measured dataset and the reference itself. Any discrepancies identified can then be used as a starting point for further investigation, helping determine whether they are related to expected system limitations or indicate issues that may require attention and troubleshooting.
Choosing the reference data is key
Independent measurements, redundant sensors, nearby measurement stations, and satellite-based irradiance models are among the most common sources of reference data. Each option has its own strengths and limitations, and each provides a different perspective on measurement quality and system performance. Depending on the assessment criteria, some reference sources may be more suitable than others.
For a more robust evaluation, it is recommended to compare measurement data against multiple independent references whenever possible. This helps provide a broader and more reliable view of data quality.
For more information on using different types of reference data, see Agreement with Reference Data Detailed Report.
How to read the results
The Test Summary section of the report provides key metrics that describe the overall agreement between the irradiance measurements and the selected reference data. Metrics such as annual bias, monthly bias, and overall dataset bias offer a quick overview of how closely the datasets align. Approximate reference values are also included for convenience and can help support a preliminary assessment of the results.
Keep in mind that the expected level of agreement depends strongly on factors such as the type of reference data used, local weather conditions, sensor class, maintenance practices, and other environmental influences.
For a more detailed analysis, including additional visualisations and pattern identification, refer to the detailed sections of the report.
Final note
The comparison with reference data should be used as a guide for understanding overall data quality, not as a standalone diagnosis. Deviations may have different causes depending on the reference source, site conditions, sensor configuration, and maintenance history. When unusual patterns or significant discrepancies are observed, they should be reviewed together with the detailed report results and, where possible, compared against additional independent references before drawing conclusions.
For more details, see Agreement with Reference Data Detailed Report.
Next, continue exploring the test summary in: Tests for Common Issues in Test SummaryTests for Common Issues in Test Summary
If you would like to review the overall report structure again, see: Read and Understand Your EKO Q Data Quality Report.