Wind and photovoltaic plants are distributed renewable energy conversion systems generating large amounts of operational data, but the indicators and forecasts derived from them are not always automatically trustworthy. Their meaning depends on what is being measured, how data are acquired and processed, whether the measurements remain valid under changing operating conditions, and how uncertainty affects their interpretation.

This Special Session will address the metrological questions that arise when field data are used to assess the performance and health of wind turbines and PV systems or to forecast their power production. It will focus on methods for verifying data quality, combining heterogeneous measurements, identifying sensor-related effects, and evaluating how measurement uncertainty propagates into derived indicators and predictions. Both established and data-driven approaches are welcome, provided that their assumptions, validation procedures and uncertainty characterization are made explicit. Wind and PV technologies provide complementary case studies. Both operate under variable environmental conditions and rely on distributed sensing, while differing in conversion processes, instrumentation and degradation mechanisms. Forecasting introduces a further common challenge: predictions depend on uncertain measurements of the plant state and the environment and are evaluated against reference observations that have their own quality limitations.

The session seeks contributions that connect measurement principles with real operating data and demonstrate how metrological choices affect monitoring, diagnosis, performance assessment and forecasting. Its aim is to move beyond treating operational datasets as self-explanatory and toward measurement results and predictions that are transparent, comparable and fit for engineering decision-making.

Topics:

• Operational data quality
• Measurement traceability
• Measurement uncertainty
• Predictive uncertainty
• Sensor validation and drift
• Multi-time scale measurements
• Multi-dimensional data processing and representation
• Heterogeneous data integration
• Performance assessment
• Condition monitoring and diagnostics
• Wind and solar power forecasting
• Reference measurements and field validation
• Benchmark datasets
• Reproducible methods