Bayesian Hierarchical Modeling: Wind Turbines and Industrial use cases

AI in Power Generation Series: Exploring Bayesian Hierarchical Models (BHM)
Welcome to the first event in our AI in Power Generation series!
While future meetups will cover topics like GenAI and Agentic workflows, we’re kicking things off by taking a deep dive into Bayesian Modeling. Join me, Ziad, for an exploration of Bayesian Hierarchical Models (BHM) and their application in building Generation Forecasting models for industrial equipment.
In this session, we’ll focus on a practical use case: modeling Wind Turbines to demonstrate the power and advantages of BHM.

### What to Expect

• Content: The session will include both slides and live Python code demonstrations.
• Tools: We’ll use PyMC for Bayesian modeling.
• Preparation: If you’re new to Bayesian frameworks, I recommend reviewing some introductory materials on PyMC beforehand, as the Bayesian approach may feel unconventional to those familiar with traditional ML methods.

### Audience

This is an advanced topic in AI and statistics, but we welcome participants of all experience levels.

### Event Details

📍 Venue: The ION (specific details to follow)
🎟️ Capacity: Limited to 60 attendees
Spots are limited, so be sure to RSVP early. Let’s dive into the fascinating world of Bayesian Hierarchical Models and uncover how they can transform power generation forecasting!