Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Multi-objective Optimization Design for Windows and Shading Configuration Considering Supply-Demand Matching
Windows and shading systems have a significant impact on the thermal and light environment in an office space. However, the design of such systems faces conflicting requirements, needing to ensure sufficient daylight without allowing too much daylight to enter the room. In addition, by maximizing the thermal and visual comfort of the users in the natural state of the building, the need for additional energy input can be reduced. This study proposes a multi-objective optimization framework, using Useful Daylight Illuminance (UDI) and Thermal Comfort Percent (TCP) as optimization objectives, to retrofit the window and shading system of office buildings for energy saving. A case study of a typical open office space in China’s hot summer and cold winter (HSCW) climate zone is conducted, employing a genetic algorithm to optimize the model and obtain multiple Pareto front solutions. These solutions are analyzed using the K-means clustering algorithm, and the optimal solution is selected through the energy consumption comparison. The results indicate that improving comfort levels in the natural building state can lead to a reduction in energy consumption. Additionally, the best-performing optimized solution shows a 5.35% decrease in energy consumption compared to the original configuration.
Multi-objective Optimization Design for Windows and Shading Configuration Considering Supply-Demand Matching
Windows and shading systems have a significant impact on the thermal and light environment in an office space. However, the design of such systems faces conflicting requirements, needing to ensure sufficient daylight without allowing too much daylight to enter the room. In addition, by maximizing the thermal and visual comfort of the users in the natural state of the building, the need for additional energy input can be reduced. This study proposes a multi-objective optimization framework, using Useful Daylight Illuminance (UDI) and Thermal Comfort Percent (TCP) as optimization objectives, to retrofit the window and shading system of office buildings for energy saving. A case study of a typical open office space in China’s hot summer and cold winter (HSCW) climate zone is conducted, employing a genetic algorithm to optimize the model and obtain multiple Pareto front solutions. These solutions are analyzed using the K-means clustering algorithm, and the optimal solution is selected through the energy consumption comparison. The results indicate that improving comfort levels in the natural building state can lead to a reduction in energy consumption. Additionally, the best-performing optimized solution shows a 5.35% decrease in energy consumption compared to the original configuration.
Multi-objective Optimization Design for Windows and Shading Configuration Considering Supply-Demand Matching
Lecture Notes in Civil Engineering
Berardi, Umberto (Herausgeber:in) / Yang, Xinyu (Autor:in) / Zhou, Xin (Autor:in) / Zhao, Jinjing (Autor:in) / Yue, Yufei (Autor:in) / Yan, Ruiyi (Autor:in) / Jiang, Yi (Autor:in)
International Association of Building Physics ; 2024 ; Toronto, ON, Canada
06.12.2024
7 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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