AI-enabled HVAC Control for Commercial and Industrial Buildings

AI-enabled HVAC Control for Commercial and Industrial Buildings

Technology Summary

This innovative system utilizes multi-tiered optimization models, including sequence-to-sequence and LSTM neural networks, to predict building occupancy and environmental conditions. It integrates seamlessly with existing building management systems to dynamically adjust HVAC settings, balancing indoor air quality and energy consumption for enhanced occupant comfort and sustainability. Deployment options include on-premises and cloud-based implementations for flexible integration

Key Advantages

• Improves indoor air quality through predictive ventilation control.
• Enhances energy efficiency by optimizing HVAC setpoints based on real-time data.
• Uses advanced neural network models for accurate occupancy and condition forecasting.
• Balances health and comfort with environmental sustainability.
• Flexible deployment options—cloud or on-premises integration.
• Seamless integration with existing building management systems.

• Reduces energy consumption caused by inefficient HVAC operation.
• Lack of predictive control based on occupancy and environmental changes.
• Reduces difficulty balancing occupant comfort with energy savings.
• Challenges in integrating advanced controls into existing building infrastructures.

Market Opportunities

• Commercial and office building HVAC management.
• Smart building and green building technology solutions.
• Energy management services for large facilities.
• Industrial and institutional buildings seeking sustainability upgrades.
• Cloud-based building control platforms and IoT integration

Stage of Development

Proof of Concept

Patent Status

Pending