Aerial Infrared Health Monitoring of Solar Photovoltaic Farms at Scale
This work addresses the need for scalable health monitoring of solar farms to improve reliability and maintenance in the renewable energy sector, representing an incremental advancement in domain-specific analytics.
The paper tackled the problem of unknown operational efficiency in solar photovoltaic farms by developing a data-driven framework using airborne infrared imagery to detect and localize defects, providing rigorous estimates of performance losses.
Solar photovoltaic (PV) farms represent a major source of global renewable energy generation, yet their true operational efficiency often remains unknown at scale. In this paper, we present a comprehensive, data-driven framework for large-scale airborne infrared inspection of North American solar installations. Leveraging high-resolution thermal imagery, we construct and curate a geographically diverse dataset encompassing thousands of PV sites, enabling machine learning-based detection and localization of defects that are not detectable in the visible spectrum. Our pipeline integrates advanced image processing, georeferencing, and airborne thermal infrared anomaly detection to provide rigorous estimates of performance losses. We highlight practical considerations in aerial data collection, annotation methodologies, and model deployment across a wide range of environmental and operational conditions. Our work delivers new insights into the reliability of large-scale solar assets and serves as a foundation for ongoing research on performance trends, predictive maintenance, and scalable analytics in the renewable energy sector.