As PV projects become larger and more complex, traditional design, construction and operation based mainly on human experience are being challenged. AI, digital twins, UAV inspection and intelligent diagnostics are entering solar system integration, shifting Photovoltaic System Integration from experience-driven to data-driven.

In the design stage, digital twins can visualize terrain, rooftops, shading, module layout, mounting structures, cable routes and equipment positions. This helps designers identify shading, drainage, maintenance access and construction conflicts in advance. For mountain PV plants, agrivoltaics, C&I rooftops and BIPV projects, real-scene modeling can significantly reduce later rework.

In the operation stage, AI can use string current, voltage, inverter efficiency, weather data and historical generation to detect soiling, hot spots, shading, inverter abnormalities, DC-side faults and generation deviations. The IEA reports that global solar PV generation increased by about 600 TWh in 2025, making PV an important source of global electricity growth. The larger installed capacity becomes, the more O&M efficiency affects total returns.
The key to AI is not automatic conclusion-making, but verifiable diagnosis. If one string shows lower output, the system should compare irradiation, temperature, neighboring strings, historical curves, cleaning records and inverter status before determining whether the cause is soiling, shading or equipment failure. A single model judging from one thermal image can easily make mistakes.

Future PV integrators need digital delivery capability. Projects should deliver not only modules and inverters, but also digital files, equipment ledgers, generation baseline models, O&M strategies and risk-warning rules. The competitiveness of Photovoltaic System Integration will increasingly depend on whether data can continuously improve generation revenue, not only on construction completion.