AI Empowers the Photovoltaic Industry: Ushering in a Smart New Era of the Energy Revolution

At the intersection of the global energy revolution and the digital intelligence wave, Artificial Intelligence (AI) has emerged as the core force driving the photovoltaic (PV) industry’s transformation from manufacturing-driven to data-intelligence-driven. China’s PV industry leads the entire industrial chain, yet it faces challenges such as homogeneous competition and efficiency bottlenecks. In-depth AI application has become the key to breaking through these dilemmas, covering the whole industrial chain including R&D, production, operation and maintenance (O&M), and dispatching — with clear cases and data-backed results across every link.

R&D: AI Accelerates Breakthroughs in New PV Technologies

AI speeds up breakthroughs in new PV technologies and drastically shortens R&D cycles. Perovskite and other next-generation batteries represent the future of the PV industry, but material screening is difficult and time-consuming. Using AI to screen perovskite material molecules compresses years-long R&D cycles to just months. Enterprises have jointly built a closed-loop R&D line featuring AI decision-making + robotic execution, advancing the mass production of perovskite-silicon tandem cells and continuously boosting cell conversion efficiency. In addition, AI combined with quantum computing enables high-throughput screening of PV materials, accurately predicting material performance and lowering R&D costs.

Production: AI Optimizes Processes and Strengthens Quality Control

On the production side, AI optimizes process parameters to improve product yield and capacity. The PV manufacturing process involves complex parameters that are hard to control precisely manually.

• AI-optimized production processes increase product reliability by 43% and shorten delivery cycles by 84%.
• AI vision inspection systems achieve a 98.7% accuracy rate for identifying defects such as hidden cracks and cold solder joints on PV cells, with inspection efficiency 420% higher than manual inspection.

Leading PV module manufacturers have deployed closed-loop AI intelligent production lines, enabling full-process data feedback and real-time adjustment to effectively resolve homogenization issues in PV module production.

Inspection: Xinghan AI Large Model Fills Industry Gaps

In PV inspection, AiJiang Technology’s Xinghan AI Large Model stands out as China’s first intelligent inspection large model for PV cells, filling the domestic gap in multi-modal intelligent inspection for PV cells. Developed with data from more than 2,000 inspection devices worldwide, it supports inspection of crystalline silicon, perovskite tandem, and other PV cell types.

• Defect recognition accuracy: 98.7%
• Capable of autonomously detecting micro-defects such as edge pinholes
• Inspection efficiency improved by over 12 times
• Helps reduce industry inspection costs by 40%

It has been applied in six major sectors and recognized by leading enterprises and research institutions.

Operation and Maintenance: AI Solves Pain Points and Delivers Significant Cost and Efficiency Improvements

AI resolves pain points in traditional PV plant O&M, cutting costs and boosting efficiency. Traditional manual inspection is labor-intensive, time-consuming, and prone to missed detections — especially in harsh environments like deserts and plateaus.

• Huawei Digital Energy’s AI O&M solution combines drone inspection and deep learning to identify 30 types of module defects with 95% accuracy, reducing single inspection time from 72 hours to 6 hours. It raises power generation by up to 10% and improves O&M efficiency by 50%.
• AI cleaning robots predict optimal cleaning times via deep learning, increasing power generation by 12%–15% and lowering O&M costs by 30%, effectively solving power loss caused by dust accumulation.

Dispatching: AI Ends “Weather-Dependent” Power Generation and Boosts Energy Utilization

In dispatching and application, AI enables efficient use of PV energy and overcomes reliance on weather conditions. By integrating meteorological and historical generation data, AI builds power prediction models with errors controlled within 10%, supporting optimized power grid dispatching.

• The Talatan PV Power Plant in Qinghai reduced its light abandonment rate from 12% to below 5% using an AI power prediction system.
• Huawei’s “PV Plant Autonomous Driving” concept uses AI for source-load-storage coordinated dispatching, lifting on-site energy self-sufficiency to over 80%.
• AI aggregates distributed PV resources to form virtual power plants (VPPs) for electricity market transactions, increasing operator revenue by 20%–30%.

Conclusion: AI Drives High-Quality Development of the PV Industry

AI applications in the PV industry have entered the large-scale deployment phase, accelerated by both policy support and market demand. The 2026 Government Work Report proposed “computing-power and electricity synergy”, promoting green PV electricity to support AI computing power and form a virtuous cycle. The global AI+PV market is growing rapidly and is projected to reach $5.3795 billion by 2029.

While challenges remain — including insufficient data standardization and a shortage of cross-disciplinary talents — the iteration and ecosystem improvement of leading technologies such as Xinghan AI will keep empowering the PV industry. AI will help PV evolve from a supplementary energy source to a mainstream power source, providing strong support for achieving carbon peaking and carbon neutrality goals.