How does AI solar power forecasting work?

The platform ingests Numerical Weather Prediction (NWP) features including Direct Normal Irradiance (DNI), shortwave radiation, cloud cover, and surface pressure from the Open-Meteo API. It passes these features into trained Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models to predict normalized photovoltaic power output.

What is the accuracy of the solar power forecasting models?

The trained XGBoost and LSTM ensemble models achieve a Root Mean Squared Error (RMSE) of 5.7% to 7.9% and an R² score exceeding 0.88 across 21 German solar facilities.

How are financial revenue and CO2 offsets calculated?

Daily financial revenue is estimated using regional day-ahead electricity spot rates (€0.15/kWh) multiplied by predicted kilowatt-hours. Carbon offsets are calculated at 0.4 kg CO2 saved per kWh produced.

AI-Powered Solar Energy Forecasting & Yield Optimization

Real-time photovoltaic capacity predictions across 21 German solar facilities powered by Open-Meteo satellite weather API.

Forecast Controls

Select PV Facility

Lat

Lon

AI Architecture

ANN Neural Network Client-side browser inference

Capacity Output 0.00%

Est. Day Revenue (€) €0.00

CO₂ Offset (kg) 0.0 kg

24-Hour Ahead Hourly Power Curve

Facility Name -

Timestamp -

Coordinates -

Normalized Value (0.0 - 1.0) -

Frequently Asked Questions & Technical Specifications

How does the AI model process weather features?

The neural network processes 49 atmospheric features extracted from Open-Meteo NWP forecasts, including Direct Normal Irradiance (DNI), Solar Zenith Angle, Cloud Cover, Dewpoint Temperature, and Surface Pressure, normalized using historical facility capacity parameters.

What benchmark accuracy do these models achieve?

Across 21 German solar facilities (nominal power 100kW to 8500kW), our ANN, LSTM, and XGBoost ensemble models achieve a Root Mean Squared Error (RMSE) between 5.7% and 7.9% with R² coefficients exceeding 0.88.