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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。

 

被動式輻射冷卻器是一種用於建築外墻的墻板,通過輻射冷卻為室内環境提供冷卻效果。輻射冷卻是將熱量從面向太空的表面釋放到宇宙的過程。這種被動的方式無需額外的能量,如電力。

當智能窗戶受到如電壓(電致變色)、熱量/溫度(熱致變色)以及光(光致變色)等外部刺激時,這些窗戶會調節自身光學和熱學的特性。在高溫的情況下,熱致變色智能窗戶會阻擋太陽輻射或在處於寒冷天氣的時候,讓太陽輻射進入室内,以保持舒適的室内環境。

各種如實地實驗和能源模擬軟件等熱分析證明了這些技術在建築節能方面深厚的潛力。安裝被動式無源輻射冷卻器和熱致變色智能窗戶能夠減少建築吸收的太陽熱量和HVAC的能源消耗,更重要的是他們無需用電并且對環境友好。

 
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。
 
根據機電工程署發表的香港能源最終用途數據2020年度報告,在2018年,空氣調節在最終用途的總能源消耗和電力消耗中分別佔最大的百分比為18%和32%。以2008年為基準至2018年,HVAC系統相關的能源消耗迅速增長了約15%。
 
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。
*視現場/驗證方式而定
 

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.