Solar Panel Soiling Forecaster
AI-based forecasting system that predicts solar panel soiling and optimizes cleaning schedules to reduce costs and improve power generation.
This use case focuses on forecasting how dirty solar panels will become based on surrounding environmental conditions and determining the optimal time to wash them.
- Air quality surface modeling using downscaling
- Predictive modeling of soiling trends
Solar farms need to balance the high cost of panel washing against electricity losses caused by soiling. Determining the optimal cleaning schedule is complex and depends on environmental conditions.
Austin AI built a forecasting system that analyzes particulate matter trends from the EPA and weather history from NOAA. The system models environmental conditions across a grid covering the United States to predict soiling buildup and calculate the optimal time to wash panels.
Environmental Data Collection
Collects particulate matter trends from the EPA and historical weather data from NOAA.
Air Quality Modeling
Uses downscaling techniques to model air quality conditions across a national grid.
Soiling Forecasting
Predicts particulate accumulation and expected panel soiling over time.
Performance Comparison
Compares modeled versus actual soiling loss to improve forecast accuracy.
Maintenance Optimization
Calculates the optimal timing for panel washing to balance cost and power output.
The system augments traditional credit reporting tools, reduces default exposure, and creates new revenue streams through structured risk intelligence.
Explore Our Use Cases
Real examples of how AI is applied to solve real business challenges.
Hedge Fund Meme Stock Rating Score
Built a system that scraped and analyzed social media data to generate sentiment-based investment signals. Applied BERT models and ticker extraction to construct portfolios based on predicted performance.
Automatic Blueprint Reader
Developed an AI system using OCR and NLP to extract structured data from construction blueprints. Reduced manual review time from hours to minutes while improving document accuracy.
Web Purchase Forecaster
Developed a predictive model to estimate purchase intent using behavioral and transactional data. Enabled businesses to optimize marketing spend and forecast revenue more accurately.
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