Various technologies have been developed for reducing energy usage in buildings, saving energy, operation cost and protecting the environment.

Innovative Energy-efficient Building Technologies:
       Passive Radiative Cooler and Smart Window

Among the various energy-efficient building technologies that have been developed through an ongoing RGC CRF project under the leadership of Prof. Christopher Chao (Dean of Engineering and Chair Professor of Mechanical Engineering at HKU), passive radiative cooler and smart window are the two innovative and promising technologies, which are especially ripe for introducing to industries.

Passive radiative cooler is a wall panel for building façade to provide cooling effect for indoor environments by radiative cooling. Radiative cooling is a process of heat removal from a sky-facing surface to the universe. It is a passive way requiring no additional energy input like electricity.

Smart window responses to external stimulus such as voltage (electrochromic), heat (thermochromic) and light (photochromic). These windows regulate their characteristics in terms of optical and thermal properties. Thermochromic smart window blocks solar radiation at high temperature or allow incoming radiation in during cold weather to maintain indoor thermal comfort.

The potential of these technologies in energy saving for buildings has been proven by different thermal analysis like field experiment and energy simulation software. Installation of passive radiative cooler and thermochromic smart window can reduce solar heat gain and HVAC energy consumption and more importantly, they require no electricity and are environmental-friendly.

一個由港大工程學院院長及機械工程講座教授 - 趙汝恆教授領導的RGC CRF項目研發了各種各樣的節能建築技術。被動式無源輻射冷卻器和智能窗戶是其中兩種創新而又成熟的技術,能夠將其引入行業。





The Use of Artificial Intelligence in
Chiller Plant Optimization
This project is led by Prof. Christopher Chao, Dean of Engineering and Chair Professor of Mechanical Engineering, from the Department of Mechanical Engineering, Faculty of Engineering et The University of Hong Kong.
The heating, ventilation and air-conditioning (HVAC) system accounts for at least 30% of total energy consumption in buildings. Traditional operation strategy of HVAC system rarely consider the actual design specification of equipment and real-life operation conditions, leading to increase in energy usage, cost and massive carbon emission. Therefore, solutions for improving the energy efficiency of HVAC system is one of the crucial topic for buildings. 
The application of artificial intelligence (AI) in chiller plant optimization is an innovative technique for energy consumption reduction and improved maintenance practice of the heating, ventilation and air-conditioning (HVAC) system, protecting the environment.
Artificial intelligence can predict the actual performance of system components and building cooling demand and the most optimized chiller operation schedule can be determined.
*Subject to site/verification measurement.
港大工程學院院長及機械工程講座教授 - 趙汝恆教授領導的一個項目探索了一種通過優化供暖、通風和空調 (HVAC) 系統冷水機組性能,從而降低能源消耗的創新方法。
為了提高冷水機組的性能,本項目通過採用人工智能技術和大數據分析為冷水機組開發了預測性操作控制策略,而當中無需安裝任何額外的設備。 人工神經網絡 (ANN) 用於預測未來趨勢,而粒子群優化 (PSO) 用於搜索冷水機組的優化設定點。
在冷水機組中採用人工智能 (AI) 可以提高整體能源效率,減少能源消耗並改善HVAC系統的維護工作,從而減少HVAC系統引起的碳排放。

We wish to collaborate with managers, buildings owners, engineers and public to adopt innovative technologies in buildings to improve energy usage and minimize the impact to the environment.