Affordable Residential Under Sunshine: A Study on the Correlation Between Affordable Housing Layout and Solar Environment in Shanghai

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dc.contributor.advisor Park, Hyoung-June
dc.contributor.author Su, Siwei
dc.contributor.department Architecture
dc.date.accessioned 2024-07-02T23:43:45Z
dc.date.available 2024-07-02T23:43:45Z
dc.date.issued 2024
dc.description.degree Arch.D.
dc.identifier.uri https://hdl.handle.net/10125/108469
dc.subject Architecture
dc.subject Sustainability
dc.subject affordable housing
dc.subject genetic algorithm
dc.subject Multi-objective optimization
dc.subject residential layout
dc.subject solar simulation
dc.title Affordable Residential Under Sunshine: A Study on the Correlation Between Affordable Housing Layout and Solar Environment in Shanghai
dc.title.alternative 阳光下的保障性住房：上海保障性住房布局与日照环境关联性研究
dc.type Thesis
dcterms.abstract Sunlight is the predominant factor limiting residential design, and the quality of sunlight is composed of both "quantity" and "quality" aspects. In the past, the evaluation of sunlight quality often focused solely on "quantity" as a measure of sunlight quality, neglecting the distinctions in "quality." This approach has largely contributed to the uniform appearance of cities. In recent years, Shanghai has vigorously promoted the construction of affordable housing and advocated enclosed layouts. Additionally, the rigid requirements for sunlight duration have been reduced, placing greater emphasis on the overall improvement of the residential environment. This has led to beneficial exploration in enhancing the morphology of residential areas.However, there is still a lack of systematic research on the correlation between enclosed layouts and sunlight environments, as well as how to guide optimal layouts through design. This paper aims to explore the possibilities of optimizing enclosed residential layouts under the guidance of different sunlight indicators. Using the Rhino + Grasshopper platform for parametric modeling of residential prototypes, sunlight simulations were conducted using the Ladybug plugin within the Grasshopper platform. The Wallacei X plugin, based on the NSGA-II algorithm, was employed for multi-objective optimization. Optimization objectives included health, comfort, energy consumption, and various other aspects to demonstrate the impact of sunlight quality on the residential environment. The optimization process assigned weights to the objectives, yielding filtered results under different weightings. In this study, a multi-objective optimization based on genetic algorithm (MOO-GA) workflow is established to optimize the design of the enclosing layout of the affordable rental residentials in Shanghai, which improves the sunlight environment of the original scheme in different dimensions under the premise of meeting the basic design requirements, and obtains diversified design results, which clearly show the correlation between the sunlight indicators and the layout design. In the establishment of this MOO-GA workflow, the actual needs of residents, the design strategies of designers, and the quantification of indicators of policy makers are integrated, which provides applicable reference to the future design mode of symbiosis between technology and people. In the future, residential experiences should be more diverse. Some individuals may prefer morning sunlight, while others may favor evening sunlight. Designers can adjust the distribution of sunlight in residential areas by controlling the weights of different optimization objectives. Residents can also choose rooms that better suit their needs based on the performance indicators of different unit types, achieving a harmonious symbiosis between technology and the living environment.
dcterms.extent 188 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:12118
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