Trusted by 100+ MWp of Solar & BESS Operations
Technical Whitepaper • 12 pages
Soiling Intelligence: Detection, Forecasting & Cleaning Optimization
Complete technical specification for physics-informed soiling detection achieving 94-97% accuracy, with cleaning schedule optimization delivering 30-35% cost reduction.
The Problem:
Solar plant operators lose €40-90K/year per 100MW from suboptimal cleaning schedules. Traditional PR monitoring achieves only 75-85% accuracy with 15-25% false positive rates, leading to over-cleaning or under-cleaning.
What You'll Learn:
- 94-97% detection accuracy validated across Spanish 120MW, UAE 50MW, and Australian deployments
- Physics-based detection: pvlib clearsky model with Haydavies transposition
- ML-enhanced prediction: LightGBM with 15 physics-informed features
- Climate-specific thresholds: Arid (5-12%), Semi-arid (3-10%), Temperate (2-7%)
- Cleaning optimization: 767% ROI, 2.7-month payback, €109K+ annual savings
- No hardware required - software-only using existing SCADA data
Based on: Research from NREL, Sandia Labs, IEEE studies, and operational data from 100+ MWp of GCC solar & BESS assets.
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