An Intelligent API Framework for Real-time Occupancy-Based HVAC Integration in Smart Building Management Systems

Abstract

There has been a consistent drive to reduce the energy consumption of buildings using machine learning and other intelligent technologies. This research proposes a unified API framework that bridges the gap between disparate smart building technologies and HVAC control systems. While current building man gement systems (BMS) offer basic integration capabilities, they lack standardized interfaces for real-time occupancy data integration and intelligent control optimization. Our framework introduces a three-layer architecture: a data integration layer that harmonizes inputs from various occupancy sensors and building systems, a processing layer that implements machine learning algorithms for occupancy prediction and pattern recognition, and a control layer that provides standardized interfaces for HVAC system optimization. The framework addresses key challenges, including protocol standardization, real-time data processing, and system interoperability. Initial implementation in a test environment comprising 50 office spaces demonstrated 27% energy savings compared to traditional BMS systems while maintaining occupant comfort levels. This research fills a critical gap in smart building infrastructure by providing a scalable, vendor-agnostic solution for intelligent building control integration. The proposed framework enables seamless integration of emerging IoT technologies and facilitates the development of more sophisticated building control strategies.