Raycatch, a start-up that has developed an AI-driven digital asset management system of solar PV assets, analyzed raw data derived from 75 geographically diverse utility-scale solar plants with a total capacity of 1.2 GW, and its findings are fairly startling:
• 65% of the plants have disconnected strings, 55% have inverters operating below their specifications, and 35% have faulty irradiance sensors.
• The average recoverable energy is 5.27%, which represents a yearly potential recoverable extra income of $160,000 per plant.
• The global annual potential savings was $6.3 billion in 2019 and is expected to reach $14.48 billion by 2024.
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Conducted by the Raycatch team, using DeepSolar, its automated diagnostics system, this industry benchmark study shows the potential of solar asset optimization through AI analysis of real production data and clear quantification of technical issues in PV plants around the world. Summed up in this Solar Asset Optimization – Industry Benchmark Study, this study presents the typical solar plant in terms of size and components, the main causes for performance issues and their scope, and it clearly quantifies the energy losses that can be recovered by smart maintenance and optimization.
Similar conclusions were made in this DNV GL report.
“The Asset Management and O&M business units in the solar industry must adopt an automated and proactive approach, and base their actions on analyzed data instead of manual processes or mere ‘feelings’. This is the only way they can thrive and strengthen their competitiveness in these challenging times and shrinking returns,” explains Haggai Hofland, Raycatch CEO and Founder. “With daily use of solar diagnostics systems that use AI to normalize and analyze the PV assets’ data, such as our own DeepSolar, it is possible to quickly identify very specific performance issues and their root-causes. Following data-driven insights and recommendations, professionals can make their solar portfolios much more efficient and get the most from their investments.”