In Hong Kong, skyscrapers are abundant, leading to significant energy consumption, with cooling systems accounting for more than half of the total power usage. Prof. Fu XIAO, Associate Dean of Faculty of Construction and Environment and Professor of Department of Building Environment and Energy Engineering at the Hong Kong Polytechnic University, has developed an award-winning AI system, which effectively helps reduce up to 40% of daily energy usage, paving the way for a greener and more energy -efficient future.
As cities strive towards carbon neutrality, the integration of artificial intelligence (AI) into building management systems is emerging as a game-changer. The ability of AI to process vast streams of data and predict future cooling demands and the performance of a building's energy systems allows it to make real-time, optimised decisions, which offers a promising path to smarter, greener and more cost-effective cooling solutions.
Prof. XIAO and her research team's AI-empowered digital twin platform for smart energy management was awarded a Gold Medal at the International Exhibition of Inventions Geneva 2025. This innovation has demonstrated substantial energy savings and operational improvements in various large buildings.
Prof. Xiao’s research, titled “An AI-enabled optimal control strategy utilizing dual-horizon load predictions for large building cooling systems and its cloud-based implementation,” was published in Energy and Buildings. The research addressed the limitations of conventional cooling system controls, which typically rely on fixed rules or single-horizon predictions. Such approaches often fail to adapt to the complex and dynamic nature of building cooling demands, leading to unnecessary energy wastage and suboptimal performance. Her innovative AI system introduces a dual-horizon load prediction strategy, leveraging both day-ahead and hour-ahead forecasts to optimise the operation of central cooling plants with multiple chillers. By combining ensemble learning and automatic machine learning (AutoML), the system generates highly accurate, probabilistic predictions of cooling loads, enabling more robust and adaptive control decisions.
Unlike traditional systems that might only rely on single-horizon predictions or fixed control rules, this AI solution uses a hierarchical approach. Day-ahead forecasts, based on predicted weather, occupancy and historical data, determine the chiller sequence and the morning start-up time for the entire plant. Hour-ahead predictions, combining real-time data with updated weather forecasts, fine-tune the start and stop times of each chiller and adjust chilled water temperatures. This dual-horizon method ensures that both long-term trends and short-term fluctuations are captured, ensuring more stable and efficient system performance.
Supported by the Electrical and Mechanical Services Department (EMSD), the proposed system has been implemented in a high-rise government office building in Hong Kong. The building's cooling system comprises multiple chillers serving both high and low zones, with a sophisticated network of pumps and heat exchangers.
The AI control strategy was deployed via a cloud-based platform, which interfaced with the existing building management system (BMS) using the BACnet protocol—a widely adopted standard for building automation. This setup allowed for seamless data collection, real-time monitoring and AI-driven optimisation, while also maintaining compatibility with the existing BMS infrastructure. Operational data from the BMS were collected at 15-minute intervals and systematically stored for easy access and analysis.
A key innovation of the system is its use of ensemble learning and AutoML to develop robust prediction models. Multiple data-driven models are trained independently, each capturing different aspects of the building's thermal behaviour and operational patterns. By combining these models, the AI can improve prediction accuracy and estimate uncertainty, enabling it to make more informed and flexible optimisation decisions.
The performance of the AI-enabled control strategy was rigorously validated through a six-week on-site test during the transition season and early summer. Achieving an average energy saving of 18.4%, the system outperformed conventional rule-based controls, with daily savings ranging from 1.1% to nearly 40%. On a typical test day, the power consumption of the chillers was reduced by up to 21%, and the coefficient of performance (COP) of the chiller plant increased by as much as 47%. These improvements were achieved without compromising thermal comfort, as the system dynamically adjusted to meet real-time cooling demands.
Detailed analysis of the test data revealed several key benefits. First, the AI system was able to reduce unnecessary chiller switching by accurately predicting when additional capacity would be needed, thus avoiding the energy wastage associated with frequent start-ups and shutdowns. Second, by optimising the chilled water supply temperature in response to predicted loads, the system improved the efficiency of the chillers. For every 1°C increase in chilled water supply temperature, the chillers achieved energy savings of approximately 3% under ideal conditions while the overall chiller plant saved about 1%.
One of the most significant outcomes of the project was its demonstration of practical, scalable AI deployment in existing buildings. The cloud-based implementation required only minor modifications to the BMS and could be rapidly deployed to other sites with similar infrastructure. This rapid deployment capability is crucial for accelerating the adoption of AI-enabled energy management across the building sector.
Beyond its implementation in the government office building, the same AI technology has been successfully applied to other large-scale cooling systems. For example, in a recent project supported by the PolyU Carbon Neutrality Funding Scheme, the AI-enabled strategy was deployed in the chiller plant at PolyU. The research, titled “Development of a probabilistic cooling load prediction-based robust chiller sequencing strategy and its real-world implementation,” was published in Applied Energy. The study shows the system reduced the average daily number of chiller switches by 56.5%, achieved daily energy savings of approximately 3,945 kWh and improved the chiller plant's COP by 4.2%. These results further underscore the robustness and generalisability of the approach, highlighting its potential for mass deployment in diverse building types.
Source: Innovation Digest
