Multi-objective HypE-GA assignment.
<div><p>The quantitative design on area and location of building façade’s windows has a significant impact on interior light and heat environment, which is also very instructive for preliminary and remodeling design of buildings. However, previous studies paid more attention to the therm...
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2025
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| _version_ | 1852022777305890816 |
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| author | Weixiang Zhang (10520078) |
| author2 | Jieli Sui (20714965) |
| author2_role | author |
| author_facet | Weixiang Zhang (10520078) Jieli Sui (20714965) |
| author_role | author |
| dc.creator.none.fl_str_mv | Weixiang Zhang (10520078) Jieli Sui (20714965) |
| dc.date.none.fl_str_mv | 2025-02-12T18:29:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0309817.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Multi-objective_HypE-GA_assignment_/28402426 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Physiology Biotechnology Evolutionary Biology Space Science Biological Sciences not elsewhere classified thermal insulation construction quantities remains stable previous studies paid rise office building pareto optimal solution different performance objectives building façade ’ </ sub >) pareto − window lighting </ p div >< p ideal design strategies different windowing strategies building energy efficiency north south window ga based study objective optimization results north window optimal design shading based performance objective others ’ results show optimized results remodeling design quantitative design design parameters xlink "> weak interactivity wall ratio sub xmlns standard floor small areas sill height sh )− research object remaining facades quickly find paper takes paper proposes office buildings objective optimized interior light higher sh heat environment geometric properties equally important clustering analysis analysis points algorithmic limitations |
| dc.title.none.fl_str_mv | Multi-objective HypE-GA assignment. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>The quantitative design on area and location of building façade’s windows has a significant impact on interior light and heat environment, which is also very instructive for preliminary and remodeling design of buildings. However, previous studies paid more attention to the thermal insulation construction and shading based on design parameters from the perspective of designers, but neglected the fact that the geometric properties of the windows themselves are equally important for building energy efficiency. Secondly, the weak interactivity and algorithmic limitations of traditional simulation platforms prevent rapid access to ideal design strategies. Therefore, this paper takes the standard floor of a high-rise office building as the research object in cold region−Yantai, facing façade windowing design, the three building performance objectives of each office unit−Annual Cooling Energy Consumption (AC), Annual Heating Energy Consumption (AH) and Annual Lighting Energy Consumption (AL)−are simulated and single/multi-objective optimized by relying on Ladybug and Honeybee (LB + HB) platform and Hypervolume Estimation Genetic Algorithm (HypE-GA) to obtain the genome of Pareto−Window-to-Wall Ratio (WWR), Window Height (WH) and Sill Height (SH)−at the lowest of each performance objective in order to determine the most energy-efficient façade windowing expression. The results show that AH and AC, their sum of quantities remains stable, are main energy consumption sources of office buildings, while the change of AL is more likely to have an impact than the others’ on Annual Totaling Energy Consumption (AT). The analysis points out that different windowing strategies can be adopted for different performance objectives. To reduce AC, priority is given to windowing on the east and north facade, with East Window-to-Wall Ratio (WWR<sub>E</sub>) at 0.2 ~ 0.3 and North Window-to-Wall Ratio (WWR<sub>N</sub>) at 0.3 ~ 0.5; to reduce AH, windows on the west and north facade should not be opened, and the remaining facades should be opened in small areas; to reduce AL, WWR> 0.7 is appropriate for each facade, and should be considered to matching a higher SH or WH; From AT, the average WWR in the single-objective and multi-objective optimization results are similar, so it is suggested that the WWR of each facade of office buildings in Yantai area is WWR<sub>E</sub> = 0.47, North South Window-to-Wall Ratio (WWR<sub>S</sub>) = 0.46, West Window-to-Wall Ratio (WWR<sub>W</sub>) = 0.18 and WWR<sub>N</sub> = 0.54. In addition, this paper proposes a method that can quickly find the Pareto optimal solution by clustering analysis on optimized results through Origin in multi-objective HypE-GA optimization study.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_8bbcdec941fe3834daa2832b0bd85c78 |
| identifier_str_mv | 10.1371/journal.pone.0309817.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28402426 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multi-objective HypE-GA assignment.Weixiang Zhang (10520078)Jieli Sui (20714965)PhysiologyBiotechnologyEvolutionary BiologySpace ScienceBiological Sciences not elsewhere classifiedthermal insulation constructionquantities remains stableprevious studies paidrise office buildingpareto optimal solutiondifferent performance objectivesbuilding façade ’</ sub >)pareto − windowlighting </ pdiv >< pideal design strategiesdifferent windowing strategiesbuilding energy efficiencynorth south windowga based studyobjective optimization resultsnorth windowoptimal designshading basedperformance objectiveothers ’results showoptimized resultsremodeling designquantitative designdesign parametersxlink ">weak interactivitywall ratiosub xmlnsstandard floorsmall areassill heightsh )−research objectremaining facadesquickly findpaper takespaper proposesoffice buildingsobjective optimizedinterior lighthigher shheat environmentgeometric propertiesequally importantclustering analysisanalysis pointsalgorithmic limitations<div><p>The quantitative design on area and location of building façade’s windows has a significant impact on interior light and heat environment, which is also very instructive for preliminary and remodeling design of buildings. However, previous studies paid more attention to the thermal insulation construction and shading based on design parameters from the perspective of designers, but neglected the fact that the geometric properties of the windows themselves are equally important for building energy efficiency. Secondly, the weak interactivity and algorithmic limitations of traditional simulation platforms prevent rapid access to ideal design strategies. Therefore, this paper takes the standard floor of a high-rise office building as the research object in cold region−Yantai, facing façade windowing design, the three building performance objectives of each office unit−Annual Cooling Energy Consumption (AC), Annual Heating Energy Consumption (AH) and Annual Lighting Energy Consumption (AL)−are simulated and single/multi-objective optimized by relying on Ladybug and Honeybee (LB + HB) platform and Hypervolume Estimation Genetic Algorithm (HypE-GA) to obtain the genome of Pareto−Window-to-Wall Ratio (WWR), Window Height (WH) and Sill Height (SH)−at the lowest of each performance objective in order to determine the most energy-efficient façade windowing expression. The results show that AH and AC, their sum of quantities remains stable, are main energy consumption sources of office buildings, while the change of AL is more likely to have an impact than the others’ on Annual Totaling Energy Consumption (AT). The analysis points out that different windowing strategies can be adopted for different performance objectives. To reduce AC, priority is given to windowing on the east and north facade, with East Window-to-Wall Ratio (WWR<sub>E</sub>) at 0.2 ~ 0.3 and North Window-to-Wall Ratio (WWR<sub>N</sub>) at 0.3 ~ 0.5; to reduce AH, windows on the west and north facade should not be opened, and the remaining facades should be opened in small areas; to reduce AL, WWR> 0.7 is appropriate for each facade, and should be considered to matching a higher SH or WH; From AT, the average WWR in the single-objective and multi-objective optimization results are similar, so it is suggested that the WWR of each facade of office buildings in Yantai area is WWR<sub>E</sub> = 0.47, North South Window-to-Wall Ratio (WWR<sub>S</sub>) = 0.46, West Window-to-Wall Ratio (WWR<sub>W</sub>) = 0.18 and WWR<sub>N</sub> = 0.54. In addition, this paper proposes a method that can quickly find the Pareto optimal solution by clustering analysis on optimized results through Origin in multi-objective HypE-GA optimization study.</p></div>2025-02-12T18:29:18ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0309817.t002https://figshare.com/articles/dataset/Multi-objective_HypE-GA_assignment_/28402426CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284024262025-02-12T18:29:18Z |
| spellingShingle | Multi-objective HypE-GA assignment. Weixiang Zhang (10520078) Physiology Biotechnology Evolutionary Biology Space Science Biological Sciences not elsewhere classified thermal insulation construction quantities remains stable previous studies paid rise office building pareto optimal solution different performance objectives building façade ’ </ sub >) pareto − window lighting </ p div >< p ideal design strategies different windowing strategies building energy efficiency north south window ga based study objective optimization results north window optimal design shading based performance objective others ’ results show optimized results remodeling design quantitative design design parameters xlink "> weak interactivity wall ratio sub xmlns standard floor small areas sill height sh )− research object remaining facades quickly find paper takes paper proposes office buildings objective optimized interior light higher sh heat environment geometric properties equally important clustering analysis analysis points algorithmic limitations |
| status_str | publishedVersion |
| title | Multi-objective HypE-GA assignment. |
| title_full | Multi-objective HypE-GA assignment. |
| title_fullStr | Multi-objective HypE-GA assignment. |
| title_full_unstemmed | Multi-objective HypE-GA assignment. |
| title_short | Multi-objective HypE-GA assignment. |
| title_sort | Multi-objective HypE-GA assignment. |
| topic | Physiology Biotechnology Evolutionary Biology Space Science Biological Sciences not elsewhere classified thermal insulation construction quantities remains stable previous studies paid rise office building pareto optimal solution different performance objectives building façade ’ </ sub >) pareto − window lighting </ p div >< p ideal design strategies different windowing strategies building energy efficiency north south window ga based study objective optimization results north window optimal design shading based performance objective others ’ results show optimized results remodeling design quantitative design design parameters xlink "> weak interactivity wall ratio sub xmlns standard floor small areas sill height sh )− research object remaining facades quickly find paper takes paper proposes office buildings objective optimized interior light higher sh heat environment geometric properties equally important clustering analysis analysis points algorithmic limitations |