RPI ID: 2015-009-401

Innovation Summary:
This invention introduces an intelligent energy management system for buildings with multiple zones and occupants. It includes distributed environmental sensors and an adaptive learning model that correlates historical HVAC behavior with comfort preferences. The system receives real-time feedback from occupants and generates optimized HVAC settings that balance comfort and energy efficiency. Using predictive algorithms, it anticipates future needs based on time of day, occupancy patterns, and energy usage goals. The architecture is modular and scalable, suitable for office buildings, academic campuses, or smart homes. It reduces overall energy consumption while respecting individual comfort boundaries.

Challenges / Opportunities:
Managing HVAC systems in shared spaces is complex due to varying user preferences and dynamic environmental conditions. Traditional control systems are static and inefficient, often leading to occupant discomfort and energy waste. This invention leverages machine learning and collaborative optimization to deliver targeted climate control. It enables fine-grained thermal zoning and occupant-aware adjustments. The approach supports smart building design goals and aligns with carbon reduction targets.

Key Benefits / Advantages:
✔ Occupant-driven energy optimization
✔ Predictive control based on learning models
✔ Reduces energy costs and enhances comfort

Applications:
• Smart buildings and offices
• Energy-conscious campuses
• Multi-tenant HVAC management

Keywords:
#energymanagement #hvac #smartbuilding #adaptivecontrol #machinelearning

Intellectual Property:
US Issued Patent 11,029,049 B2