| 摘要: |
| 采光是智能遮阳系统的重要调控对象，
而调控方法是智能遮阳设计的重要内容。研究梳
理了以天然采光为导向的智能遮阳调控发展脉
络，并总结了智能遮阳调控设计要点，提出结合
当下机器学习技术的智能遮阳调控方法。通过脉
络梳理，明晰了由物理实测采集闭环到数字模型
预测开环的调控发展趋势，以及立足人工智能时
代语境下趋向机器学习技术的交叉创新运用趋
势。最后，研究基于系统性梳理，获取了智能遮阳
采光调控设计要点，并结合机器学习相关技术，
进一步提出机器学习辅助下的智能遮阳采光调控
设计方法。为建筑智能遮阳调控设计和创新提供
了技术性、科学性和可行性支持。 |
| 关键词: ：建筑智能遮阳 调控系统 物理性能 数字技术 人工智能 |
| DOI：10.13791/j.cnki.hsfwest.20240518 |
| 分类号: |
| 基金项目:国家自然科学基金青年科学基金项目（52408014）；
哈尔滨工业大学助理教授科研启动项目（AUGA
5630109423）） |
|
| The development and methods of smart shading control guided by daylighting optimization |
|
LUO Zhaoyang,LIU Jing,XIE Wenlong,QI Xuanning,YANG Jingchao
|
| Abstract: |
| Daylight holds significant importance in architecture, not only enhancing the visual
and mental well-being of occupants within living spaces but also effectively reducing the energy
consumption of artificial lighting within buildings. The exterior surface of a building plays a decisive
role in creating the indoor lighting environment. It responds to changes in outdoor lighting and
coordinates the interaction between indoor and outdoor light. However, traditional building surfaces
have long relied on manual adjustments during operation, unable to dynamically respond to changes
in outdoor lighting, hence struggling to maximize the benefits of daylight. To address this issue,
recent academic efforts have combined intelligent technology, initiating research into smart shading
control.
Smart shading is a pivotal component within smart buildings. By integrating Building
Automation Systems (BAS) and Building Information Models (BIM), it can intelligently regulate
indoor daylight. Existing research emphasizing smart shading practices aligned with optimizing
daylight predominantly focuses on the form of shading and innovative materials but lacks indepth
exploration of control methods. The control methods of smart shading significantly
impact the performance of daylight utilization, directly determining the upper limit of smart
shading performance. However, existing research on control methods still falls short in practical
implementation, requiring improvement in efficiency, adaptability to various scenarios, precision in
performance enhancement, synergy among multiple functionalities, and control cost reduction.
Hence, there is a need to explore effective control strategies for optimizing indoor daylight
within smart shading systems. This involves establishing universal principles for different smart
shading control processes, incorporating new technologies and methodologies to innovate smart
shading control methods. This approach aims to enhance existing smart shading control performance
and overcome current limitations.
The advent of digital technology has revolutionized the expansion of knowledge and
technological upgrades in architecture. This evolution is evident not only in the generation of
designs driven by digital performance but also in the intelligent construction and operation of built
environments. Consequently, architectural design for living spaces has evolved from “form design”
to “control design”. On another front, advancements in digital technology, particularly artificial
intelligence, continuously break application barriers, driving the innovation of high-precision, highefficiency
human-machine interactive dynamic control systems.
The study outlines the developmental trajectory of smart shading control focused on optimizing
daylight and summarizes key design points for smart shading control, proposing smart shading
control methods integrating current machine learning technologies. By outlining this trajectory,
it elucidates the trend of control development from physical measured closed-loop systems to
predictive open-loop digital models and the inclination toward cross-innovation utilizing machine
learning technologies in the context of the artificial intelligence era. Machine learning’s capability to
effectively 摵楮捣慯瑶潥牲?浣慯灭灰楬湥杸猠??慴湴摥?潮灳琠楩浮椠穥楮湶杩?摯慮祭汥楮杴桡瑬?捬潩湧瑨牴漠汩?杦潯慲汭獡?睩楯瑮栠楡湮?猠浭慡牮瑡?獥栠慮摯楮湬杩?捥潡湲瑩牴潩汥??味潩?敮晩晦敩捣瑡楮癴敬汹礠?晩潤牳洠畩汮愠瑲敥?楯湬瑶敩汮汧椠杩敳湳瑵?獳栠慳摵楣湨朠?慳渠摭?摬慴祩氭楤杩桭瑥?捳潩湯瑮牡潬氠?摡整獡椠杲湥?浵散瑴桩潯摮猠?慮湤搠?慮楣摬?獡瑲爠慣瑯敲杲祥?晡潴物浯畮氊慭瑡楰潰湩?晧潳爠?獮洠慳牭瑡?獴栠慳摨楡湤杩?潧瀠散牯慮瑴楲潯湬?搠畴牨楥湲来?瑹栠敧?慥牡捴桬楹琠敡捤瑶畡牮慣汩?摧攠獩楴杳渠?獲瑡慣杴敩??瑬栠楡獰?牬敩獣敡慴物捯桮?瀊版敯獷敥湶瑥獲?椠湣景潮牳浴慲瑵楣潴湩?慧渠慭污祣獨楩獮?洠敬瑥桡潲摮獩?执愠獰敲摥?潩湣?晩敶慥琠畭牯敤?獬敳氠敦捵瑮楤潡湭??浴潡摬敬汹?灥牮整摡楩捬瑳椠潡渠?浹敭瑢桩潯摴獩?扰慲獯散摥?潳渠?浥畴汷瑥楥灮氠敤?慴污朠潡牮楤琠桡浬獧?楲湩?灨慭牳愮氠汔敨汥??慬湴摩?潡扴橥攠捡瑣楣癵敲?潣灹琠楡浮楤稠慥瑦楦潩湣?浥敮瑣桹漠摯獦?扴慨獥敳摥?潭湯?灥牬潳砠祲?浬潹搠敮汯獴??呮桬敹猠敯?洠整瑨桥漠摡獲?捨潩汴汥散捴瑵楲癥攠汤祥?獩畧灮瀠潯牦琠?瑨桥攠?瑬敧捯桲湩楴捨慭汳??獵捴椠敡湬瑳楯昊楯据??慨湥搠?晵敡慬獩楴批氬攠?慵獡灮整捩瑴獹?漠晡?楤渠湡潴癴慲瑩楢潵湴?楳渠?慦爠捴桨楥琠敤捡瑴畡爠慩汴?楥湬瑦攮氊汅楦杦敥湣瑴?獶桥慬摹椠湡杰?捬潹湩瑮牧漠汭?摣敨獩楮来渠?earning algorithms to daylight prediction and shading control necessitates a clear understanding of fundamental aspects
in smart shading control design. This includes defining basic principles in control design processes, facilitating the effective construction of smart shading
control systems in different architectural lighting environments.
From the developmental perspective of smart shading control and irrespective of its branch type, it encompasses variables influencing daylight
performance, optimizing daylight goals, and dynamic daylight control methods, which serve as inputs, outputs, and control components, each highlighting
specific trends in technical content development. In response to these trends, this research proposes current design points for intelligent shading and
daylight control, encompassing determination of daylight environmental parameters, mapping of daylight environmental indicators, and multi-performance
optimization within daylight environments.
In conclusion, through a systematic review, the study has identified key design aspects in intelligent shading and daylight control, further proposing
intelligent shading and daylight control design methods augmented by machine learning-related technologies. The robust data analysis and underlying
information mining abilities of machine learning empower technical enhancements in determining daylight environmental parameters, constructing daylight
in |
| Key words: building smart shading control system physical properties digital technology artificial intelligence |
