Overview
Developed a comprehensive 8760-hour energy simulation model for predicting solar power plant generation. This tool is used for feasibility studies, financial modeling, and risk assessment.
Key Features
- Hourly generation profiling based on solar resource data
- Degradation modeling over project lifetime
- Performance ratio (PR) analysis
- Loss breakdown (soiling, shading, wiring, inverter)
- Sensitivity analysis for key variables
Technical Implementation
The model uses Python with Pandas for data processing, integrating with PVsyst simulation outputs for validation. Monte Carlo simulations provide probability distributions for different scenarios.
Results
- Validated against 15+ operational projects
- 99.2% accuracy in Year 1 predictions
- Reduced feasibility study time by 60%