AI-Driven Machine Learning Framework
Optimizing Emissions and Energy Efficiency in Power Plants
Organized by: Research and Education Promotion Association (REPA) LLC, USA
In association with: IEEE Sustainable Energy and Intelligent Engineering Society (SEIES), Okinawa, Japan
Date and Venue: April 16, 2026 – 3:00 PM (PST), University of British Columbia (UBC), Vancouver, Canada
The AI-Driven Machine Learning Framework Workshop, a featured technical session under SIP2026 – The International Conference on Sustainable Innovations and Partnerships, focuses on how Artificial Intelligence (AI) and Machine Learning (ML) can transform energy efficiency, reduce emissions, and support sustainable power generation.
Organized by REPA LLC (USA) and IEEE-SEIES (Japan), the workshop will introduce advanced AI frameworks that enhance real-time monitoring, predictive maintenance, and intelligent control systems in modern energy plants. It aims to connect researchers, engineers, and professionals in developing innovative solutions aligned with the United Nations Sustainable Development Goals (SDGs).
Core Themes
- Integration of AI and ML for emission reduction
- Predictive algorithms for power plant optimization
- Digital twin and IoT-based intelligent monitoring
- Renewable energy forecasting and automation
- Smart policy and engineering approaches for sustainability
Workshop Objectives
- Showcase AI-driven tools for energy efficiency and emission control
- Foster interdisciplinary collaboration among experts and institutions
- Promote sustainable engineering through intelligent digital systems
Speaker:
Dr. Mir Sayed Shah Danish
Research & Innovation Chair, REPA LLC, USA
Lead Organizer – SIP2026 Conference
Expected Outcomes:
- Understanding AI frameworks for sustainable power systems
- Knowledge of ML implementation for operational efficiency
- Networking opportunities with IEEE, REPA, and global experts
Keywords: