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!

• 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:

  • Building BP201, BP205, (BP106) Data Repository – 2024/1 

  • Building BP201, BP205, (BP106) Data Repository – 2024/2

• Content: Sensor time-series including temperature, humidity; setpoints and operational parameters, energy use etc.

• Access Platform: Kaggle

• Support Materials: Metadata, documentation

• 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