The Smart Buildings ChallengeLearning Foundation Models for Real-World Optimization at Scale

Buildings cause nearly 40% of carbon emissions in the U.S. Of that total, HVAC systems are responsible for roughly 40 to 60%, and account for about 15% of global energy consumption. Yet despite advances in AI, most buildings still run about as inefficiently as they did decades ago.

The biggest obstacle is the lack of policy transfer. Most building control systems, whether simulation-based or learned, only work well for the building they were developed for, and fail to generalize to others.

To break through this, we propose a challenge to develop building foundation models that adapt to new buildings with minimal configuration, so building optimization can finally scale globally.

Participants build control policies on a set of simulated buildings, then have those policies evaluated on an unseen set of buildings that vary in size, layout, weather, material properties, and HVAC system. Success here would mark a real leap for global sustainability and show what ML can do against climate change.

The official competition rules are on the Kaggle rules page.