NeurIPS 2026 Smart Buildings Competition
University of Washington
📌 About the Event
The NeurIPS 2026 Smart Buildings Competition challenges participants to develop machine-learning models that optimize HVAC systems across different commercial buildings. Organized by researchers from leading academic and industry institutions, the challenge focuses on reducing energy costs and carbon emissions while maintaining occupant comfort. Participants build control policies that must generalize to unfamiliar building layouts, equipment and weather conditions.
ℹ️ Event Details
- Organiser: University of Washington, with the competition hosted by the NSF AI Institute in Dynamic Systems and academic and industry collaborators
- Competition Type: Machine Learning / AI Competition
- Theme: Smart Buildings, HVAC Optimization and Sustainability
- Mode: Online
- Registration Fee: Free
📅 Important Dates
- Competition Start Date: 13 October 2026
- Research Paper Submission Deadline — First Round: 13 November 2026
- Entry Deadline: 3 January 2027
- Final Submission Deadline: 15 January 2027
🎯 Eligibility and Rules
- Open to participants interested in machine learning, control systems, building optimization and sustainability.
- Participants must develop a control policy that generalizes to previously unseen commercial buildings and weather conditions.
- Solutions must balance occupant comfort, energy costs and carbon emissions.
- Code submissions must be made through Kaggle Notebooks and comply with the competition’s runtime and internet-access requirements.
- Participants must accept the official competition rules before the entry deadline.
- The research-paper track has separate submission deadlines and award criteria.
🏆 Benefits for the Participants
- Total Prize Pool: US $40,000 across the main leaderboard and research-paper awards.
- Main Leaderboard Prize Pool: US $25,000.
- First Prize: US $5,750.
- Second Prize: US $3,750.
- Third Prize: US $3,000.
- Research Paper Awards: US $15,000 in total across the first-round and final paper awards.
- Practical Experience: Apply machine learning to building energy optimization, HVAC control and sustainability.
- Research Opportunities: Develop and present innovative approaches to a real-world optimization challenge.