As global solar adoption grows, photovoltaic (PV) system underperformance and degradation remain key challenges, impacting energy yield and financial returns. Solar PV systems degrade over time due to factors related to manufacturing defects, environmental exposure and poor maintenance. While targeted interventions exist to mitigate specific degradation mechanisms, the primary challenge lies in accurate diagnosis. Traditional diagnostic methods, such as drone-based thermal imaging and on-site inspection, are often hardware-intensive and costly, limiting their scalability for large solar portfolios.
To address these challenges, the technology owner has offered an AI-powered hardware-agnostic software platform that analyzes inverter data to identify degradation mechanisms and assess PV system health. Leveraging advanced machine-learning algorithms, the platform can evaluate degradation rate, system inefficiencies, and root causes of underperformance, providing solar asset owners with real-time insights and actionable recommendations to optimize system performance. The solution goes beyond simple diagnostics, it integrates financial modeling and power output forecasting to help users make data-driven decisions to maximize energy yield and their solar investment returns.
The technology owners are actively seeking collaborations with solar operation & maintenance (O&M) providers aiming to enhance their maintenance packages with AI-driven PV health check and predictive diagnostics.