Curriculum
- 4 Sections
- 16 Lessons
- 1 Day
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- Foundations of Wind Forecasting4
- 1.1Evolution of wind energy forecasting: From statistical models to AI-led prediction
- 1.2Key forecasting terminologies: Wind shear, turbulence intensity, spatial variability
- 1.3AI/ML concepts in context: Neural networks, time-series learning, ensemble models
- 1.4Introduction to environmental sentinels: LIDARs, satellite telemetry, smart sensors
- Situational Awareness in AI-Based Forecasting4
- Use Cases4
- Interactive Simulations4
- 4.1Group Team: Map environmental sensor placement for optimal forecast granularity
- 4.2Simulation: Feed weather and turbine data into a simplified AI model for forecast testing
- 4.3Diagnostic drill: Identify root causes of a sudden under-forecasted power drop
- 4.4Wrap-up panel: Expert Q&A on real-world deployment challenges