
AI-Based Modeling for Energy-Efficient Buildings Challenge
Challenge Details
About the Challenge
The challenge focuses on developing AI-based prediction for chilled water plants in large buildings to:
- Use machine learning to accurately predict HVAC (Heating, Ventilation and Air Conditioning) system loads, in particular cooling demand
- Bridge the gap between academic AI and real-world deployment
- Promote transparency, generalisability, and feasibility of solutions for real building operations
💡 The load prediction model can serve as basis for future building control optimisation methods. After this competition about the load prediction task, we are planning a second competition about improving the current building control strategy, based on the present load prediction task. Stay tuned!
Key Dates
- Registration starts: September 1, 2025
- Submission deadline: November 16, 2025
Dataset & Resources
- Dataset: Real building operational data collected in 2024 & 2025 from a large industrial building at RBHU Campus Budapest
- Public Repository: Historical data from 2024 available on ZENODO for reference:
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Content: Sensor time-series including temperature, humidity; setpoints and operational parameters, energy use etc.
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Access Platform: Kaggle
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Support Materials: Metadata, documentation
Expected Outcomes
- Participants will:
- Develop load prediction models
- Gain visibility through public sharing of top solutions and final workshop
- Have opportunities for publication and recognition within the ELIAS project network
How to Participate
Where: Kaggle
Who: Open to individuals and teams from academia, startups, and industry
Steps:
- Register on Kaggle and accept the rules
- Access the datasets and baseline tools
- Submit your results as per the format
Have Questions?
Partners and Support
- ELIAS project consortium members
- Bosch Corporate Research and Global Real Estate units
- Universities and Research Partners
- Kaggle (as challenge platform)
FAQ
- Q: Who is eligible to participate?
- A: Anyone, including students, professionals, and researchers, can register on Kaggle and join.
- Q: Can teams join?
- A: Yes, team participation is encouraged.
- Q: What tools can be used?
- A: Any tools/languages that support reproducible machine learning
- Q: Is the competition free?
- A: Yes, participation is free of charge.