Developing a Novel Sensor Health KPI for Evaluating Irradiance Sensor Performance

Reliable irradiance measurements are essential for accurate performance analysis in PV systems, influencing everything from system health diagnostics to financial forecasting and energy modeling. However, irradiance sensors such as pyranometers or reference cells can be impacted by a range of issues including miscalibration, soiling, misalignment, and data corruption. To address this challenge, we introduce the Sensor-Health KPI—a data-driven metric designed to evaluate and rank the data quality and reliability of each irradiance sensor in a PV system daily. The proposed KPI incorporates six evaluation parameters encompassing general data quality metrics and irradiance related performance attributes. Each parameter is individually scored, weighted, and aggregated to yield a normalized KPI score ranging from 0 to 100. This framework is integrated into the photovoltaic analytics software Solar –AI Analytics Univers. The method fills a gap in current PV monitoring standards such as IEC 61724-1:2021 by providing a structured methodology for sensor selection in performance assessments. Ultimately, the Sensor-Health KPI enables more robust downstream calculations, leading to improved performance evaluation and operational decision-making in PV systems.