TUTORIALS

Tutorial session 1 : Explainable AI for Sustainable Energy Output

Prediction : An End-to-End Tutorial

Tutorial organizers

Lotfi Snoussi, Tunisia
Olfa Fakhfakh, Tunisia

Subject Area :

Artificial Intelligence for Green Energy
Machine Learning and Ensemble Methods for Energy Systems
Explainable AI for Sustainable Power Systems

Main Objectives :

The digital transformation of the energy sector is accelerating the adoption of Artificial Intelligence to enhance efficiency, reliability, and sustainability. Accurate energy output prediction has become a critical component of modern power systems, enabling better operational planning, performance optimization, and smarter integration of renewable resources.

This tutorial offers a hands-on, end-to-end introduction to Machine Learning for energy output prediction using a real-world dataset. Participants will build a complete predictive pipeline—from data exploration and preprocessing to advanced model development, evaluation, and comparison.

The session combines regression techniques, powerful ensemble learning methods, and Artificial Neural Networks to model complex nonlinear relationships in energy systems. Emphasis is placed on robust validation strategies, hyperparameter optimization, and generalization analysis to ensure reliable and scalable solutions.

In addition, the tutorial introduces practical Explainable AI (XAI) tools to interpret model behavior and understand the influence of key variables on energy output. This transparency is essential for deploying trustworthy AI systems in real-world energy environments.

Through guided demonstrations and reproducible Python implementations, participants will gain practical expertise in designing data-driven models tailored to green energy challenges. The tutorial bridges theoretical AI concepts with operational energy applications, equipping researchers and engineers with actionable skills for building intelligent and sustainable energy systems.

Brief Description of the tutorial proposal

This tutorial provides a comprehensive and practical introduction to applying Machine Learning techniques for energy output prediction using a real-world dataset. It covers the complete ML workflow while integrating regression models, ensemble learning methods, neural networks, and Explainable AI (XAI).

The main objectives are:

To understand the complete Machine Learning pipeline for energy prediction.
To implement and compare regression and ensemble models.
To apply Artificial Neural Networks for nonlinear energy modeling.
To optimize and validate models using cross-validation and hyperparameter tuning.
To interpret model behavior using Explainable AI techniques.
To design reliable and trustworthy AI-driven energy forecasting systems.