Program Overview
This high-impact, expert-led program equips professionals in the wind energy sector with the knowledge and tools to adopt AI-driven forecasting techniques using environmental sentinels such as LIDAR, satellite telemetry, and IoT-based weather sensors. With a balance of conceptual learning, real-world case studies, and hands-on simulations, participants will explore how predictive analytics and machine learning can mitigate wind variability risks, improve power yield estimations, and support grid stability. The program is specifically designed to solve real-world forecasting challenges faced by wind farm operators, utility planners, and analytics teams.
Features
- Understand how AI and sensor networks are transforming wind forecasting accuracy
- Analyze wind forecasting challenges across varying terrains and climates
- Apply data from environmental sentinels to improve predictive decision-making
- Simulate AI-driven forecast models to solve real-world operational issues
Target audiences
- Wind Farm Operations & Maintenance Engineers
- R&D Engineers focused on wind modeling
- Renewable Energy Data Scientists
- SCADA/EMS Analytics Teams
Curriculum
- 4 Sections
- 16 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- 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
Instructor
