AI predicts weather patterns for solar energy systems by leveraging advanced machine learning models that process vast amounts of data from various sources. Here’s a step-by-step explanation of how this works:
Process Overview
1. Data Collection
AI models gather data from several sources:
- Satellite Imagery: Provides cloud cover and atmospheric conditions.
- Ground-Based Weather Stations: Offers real-time weather data.
- Historical Production Data: Includes past power output from solar panels.
- Asset Properties: Details like panel orientation and number of panels.
2. Data Processing
- Feature Engineering: Designing meaningful inputs for the models.
- Data Filtering: Removing irrelevant or noisy data to ensure model accuracy.
- Model Training: Using years of historical data to teach the model.
3. Forecasting
- Model Deployment: AI models generate forecasts based on learned patterns.
- Continuous Improvement: Models retrain regularly to adapt to new data.
Technologies and Systems
- Climavision’s Horizon AI: Offers subseasonal-to-seasonal and short-term forecasts for energy companies.
- Aardvark Weather: A fully AI-driven system that is faster and more efficient than traditional weather forecasting systems.
Improvements
- AI improves forecasts by correcting errors in traditional weather models.
- Enhancing predictive accuracy helps optimize solar power supply and demand.
This technological advancement in weather forecasting is crucial for optimizing solar energy production and ensuring grid stability, particularly in volatile markets.
Original article by NenPower, If reposted, please credit the source: https://nenpower.com/blog/how-does-ai-predict-weather-patterns-for-solar-energy-systems/
