Physics & Data Engine Overview
Simulation Architecture
How Weather Data Becomes
Expected Energy Output
See the technical workflow of our AI-driven engine. We process raw GHI parameters into bankable P50/P90 energy yields and financial projections.
01
GHI Data Aggregation
5+ Years of Historical Climate Processing
- Acquire site-specific coordinates via map pin or direct input.
- Fetch 5+ years of Global Horizontal Irradiance (GHI) data to map true weather trends.
- Extract granular hour-by-hour metrics including ambient temperature and diffuse radiation.
02
POA Irradiance Calculation
Determining True Solar Exposure
- Translate GHI into Plane of Array (POA) irradiance using structural Tilt and Azimuth parameters.
- Apply transposition algorithms to account for the physical angle of the solar arrays.
03
Dynamic Loss Modeling
Applying Engineering Derates
- Calculate thermal losses dynamically hour-by-hour using ambient weather vs panel NOCT ratings.
- Factor in geographical soiling, wiring (ohmic) resistance, and inverter clipping/efficiency limits.
System_Parameters.cfg
04
Expected Energy Numbers
P50 vs. P90 Confidence Intervals
- P50 Yield: The baseline 50% probability estimate, indicating standard expected generation (kWh/year).
- P90 Yield: The conservative 90% confidence estimate used by banks to underwrite solar financing.
05
Financial Analysis
ROI & Investment Economics
- Translate generation data into monetary value using local grid tariffs and system costs.
- Automatically calculate critical sales metrics: Payback Period, IRR, and 25-Year Lifecycle Savings.