AI Forecasting Methods for Renewable Energy Generation

Course Description:

This course provides professional engineers with a comprehensive technical understanding of artificial intelligence–based forecasting methods used in renewable energy systems. The course begins by examining the physical drivers of renewable energy variability and the atmospheric processes that influence wind and solar generation. Traditional forecasting techniques, including persistence models, numerical weather prediction systems, and statistical time-series methods, are introduced as foundational approaches that historically supported renewable forecasting efforts.

Learning Objectives:

Upon completion of this course, the participant will be able to:

1. Explain the atmospheric and environmental factors that influence variability in wind and solar power generation.
2. Analyze how renewable energy variability affects power system stability, grid balancing requirements, and operational planning.
3. Distinguish between traditional renewable forecasting approaches—including persistence models, numerical weather prediction systems, and statistical time-series techniques—and modern AI-based forecasting methods.
4. Identify the major data sources used in renewable forecasting systems, including SCADA operational data, meteorological observations, satellite imagery, and numerical weather prediction outputs.
5. Evaluate the role of data engineering processes such as data synchronization, preprocessing, feature engineering, and data validation in developing reliable forecasting models.
6. Describe the structure and function of machine learning forecasting models, including neural networks, decision tree models, and ensemble learning methods used to predict renewable generation.
7. Explain advanced AI architectures used for time-series forecasting of renewable energy production, including recurrent neural networks, long short-term memory networks, convolutional neural networks, and transformer-based models.
8. Assess how probabilistic forecasting methods quantify uncertainty in renewable energy predictions and support risk-informed operational decision-making.
9. Analyze how renewable energy forecasts are used in grid operations, including unit commitment planning, economic dispatch, reserve allocation, and transmission system management.
10. Evaluate the role of renewable forecasting in electricity market participation, generation bidding strategies, and imbalance risk management.
11. Interpret statistical performance metrics used to validate forecasting models, including mean absolute error, root mean square error, bias analysis, and probabilistic forecast evaluation methods.
12. Apply engineering judgment to real-world renewable forecasting scenarios involving wind farms, solar photovoltaic facilities, and integrated renewable generation portfolios.