A generic framework and strategies for integrating AI into building automation systems for field-level optimization of HVAC systems

Abstract

With the growing demand for energy-efficient and optimized building operations, AI-based control optimization for HVAC systems has garnered increasing attention. However, most existing approaches remain confined to academic research due to challenges in practical deployment and the operational reliability of AI-driven strategies. This paper, presents a generic framework and associated strategies for integrating AI-driven online control optimization into Building Automation Systems (BAS) by adopting AI-enabling smart control stations at the BAS field level. The proposed framework comprises two core functional modules: (1) an AI operating environment that supports lightweight and, real-time execution of AI models, and (2) a comprehensive suite of AI functional boxes that ensure effective and reliable execution of AI algorithms. The framework's functionalities are validated by integrating two smart control stations with a BAS testbed. Control robustness and energy performance are evaluated through hardware-in-the-loop testing using a simulated dynamic HVAC system. The test results demonstrate that the proposed framework and AI-driven strategies can maintain robust and stable control under various critical conditions, while achieving a 7.66 % reduction in energy consumption.