Heating, Ventilation and Air Conditioning (HVAC) systems play a crucial role in smart building management, but their energy consumption is a significant concern. Prof. Shengwei WANG, Chair Professor in Building Energy and Automation and Otto Poon Charitable Foundation Professor in Smart Buildings in the Department of Building Environment and Energy Engineering at The Hong Kong Polytechnic University has designed a versatile framework to integrate AI into building automation systems to improve energy efficiency.
HVAC systems are particularly important to smart buildings, maintaining comfortable indoor. In Hong Kong, where commercial buildings account for a significant portion of the city’s energy use, HVAC systems alone can consume up to 40% of a building's total energy. Traditional control methods, such as simple on-off switches or Proportional-Integral-Derivative controllers, often fall short in optimising these complex and dynamic systems. As a result, there is a growing interest in leveraging artificial intelligence (AI) to enhance HVAC performance, reduce energy consumption and support the city's broader sustainability goals.
Significantly, Prof. WANG and his research team addressed a pressing question: How can AI be reliably and practically integrated into building automation systems (BAS) to optimise HVAC operations at the field level, rather than relying on remote servers or cloud-based solutions? Their answer lies in a novel, generic framework that brings AI-driven optimisation directly to the edge, exactly where the action is. The research, titled “A generic framework and strategies for integrating AI into building automation systems for field-level optimization of HVAC systems,” was published in Energy.
Prof. Wang's study introduced a practical approach for embedding AI into BAS by deploying smart control stations at the field level. These stations are designed to host and execute AI algorithms in real-time, enabling adaptive, data-driven control of HVAC systems without the latency or reliability issues associated with cloud computing.
The framework comprises two main components: an AI runtime environment for model inference and learning, and a suite of functional modules for data handling, task management and system robustness. By integrating these smart stations into a testbed and a hardware-in-the-loop simulation of a large-scale cooling system, the research demonstrates not only the feasibility but also the tangible benefits of this approach.
Compared to a conventional baseline strategy using fixed setpoints, the AI-enabled optimisation achieved a 7.66% reduction in total energy consumption. Specifically, the optimised control strategy resulted in an average increase of 2.30 K in the cooling water return temperature and a decrease of 1.93 K in the supply temperature. These adjustments led to a reduction in cooling water pump energy use, even as seawater pump consumption rose slightly, a trade-off that made sense given the system's characteristics, where cooling water pumps are the primary energy consumers due to long-distance piping. By continuously balancing the energy use of pumps and chillers, the AI-driven approach delivered significant efficiency gains without compromising occupant comfort.
The significance of these findings extends beyond the laboratory. By demonstrating that AI-driven optimisation can be reliably executed at the field level, using affordable, off-the-shelf hardware and without modifying existing BAS infrastructure, this research provides a scalable and non-intrusive pathway for smart building operators to adopt advanced control strategies.
Prof. Wang's work marks a significant step forward in the practical application of AI for building energy management. By bringing intelligence to the edge, the proposed framework not only delivers measurable energy savings, 7.66% in a demanding Hong Kong context, but also ensures the robustness and stability required for real-world deployment.
Source: Innovation Digest 7
