Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency

Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for...
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Autor*in:	Bahdad, A. A. S. [verfasserIn] Fadzil, S. F. S. Onubi, H. O. BenLasod, S. A.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Building performance Daylight availability Genetic algorithm Multi-objective optimization Parametric design

Anmerkung:	© Islamic Azad University (IAU) 2021

Übergeordnetes Werk:	Enthalten in: International journal of environmental science and technology - Tehran : Islamic Azad University, 2004, 19(2021), 4 vom: 28. Apr., Seite 2659-2676
Übergeordnetes Werk:	volume:19 ; year:2021 ; number:4 ; day:28 ; month:04 ; pages:2659-2676

Links:	Volltext

DOI / URN:	10.1007/s13762-021-03328-9

Katalog-ID:	SPR046552782

Internformat


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520			\|a Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model.
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allfields	10.1007/s13762-021-03328-9 doi (DE-627)SPR046552782 (SPR)s13762-021-03328-9-e DE-627 ger DE-627 rakwb eng Bahdad, A. A. S. verfasserin (orcid)0000-0001-7673-7728 aut Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Islamic Azad University (IAU) 2021 Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. Building performance (dpeaa)DE-He213 Daylight availability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Parametric design (dpeaa)DE-He213 Fadzil, S. F. S. aut Onubi, H. O. (orcid)0000-0001-5353-1276 aut BenLasod, S. A. aut Enthalten in International journal of environmental science and technology Tehran : Islamic Azad University, 2004 19(2021), 4 vom: 28. Apr., Seite 2659-2676 (DE-627)510463398 (DE-600)2230399-6 1735-2630 nnns volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676 https://dx.doi.org/10.1007/s13762-021-03328-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2021 4 28 04 2659-2676
spelling	10.1007/s13762-021-03328-9 doi (DE-627)SPR046552782 (SPR)s13762-021-03328-9-e DE-627 ger DE-627 rakwb eng Bahdad, A. A. S. verfasserin (orcid)0000-0001-7673-7728 aut Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Islamic Azad University (IAU) 2021 Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. Building performance (dpeaa)DE-He213 Daylight availability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Parametric design (dpeaa)DE-He213 Fadzil, S. F. S. aut Onubi, H. O. (orcid)0000-0001-5353-1276 aut BenLasod, S. A. aut Enthalten in International journal of environmental science and technology Tehran : Islamic Azad University, 2004 19(2021), 4 vom: 28. Apr., Seite 2659-2676 (DE-627)510463398 (DE-600)2230399-6 1735-2630 nnns volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676 https://dx.doi.org/10.1007/s13762-021-03328-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2021 4 28 04 2659-2676
allfields_unstemmed	10.1007/s13762-021-03328-9 doi (DE-627)SPR046552782 (SPR)s13762-021-03328-9-e DE-627 ger DE-627 rakwb eng Bahdad, A. A. S. verfasserin (orcid)0000-0001-7673-7728 aut Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Islamic Azad University (IAU) 2021 Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. Building performance (dpeaa)DE-He213 Daylight availability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Parametric design (dpeaa)DE-He213 Fadzil, S. F. S. aut Onubi, H. O. (orcid)0000-0001-5353-1276 aut BenLasod, S. A. aut Enthalten in International journal of environmental science and technology Tehran : Islamic Azad University, 2004 19(2021), 4 vom: 28. Apr., Seite 2659-2676 (DE-627)510463398 (DE-600)2230399-6 1735-2630 nnns volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676 https://dx.doi.org/10.1007/s13762-021-03328-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2021 4 28 04 2659-2676
allfieldsGer	10.1007/s13762-021-03328-9 doi (DE-627)SPR046552782 (SPR)s13762-021-03328-9-e DE-627 ger DE-627 rakwb eng Bahdad, A. A. S. verfasserin (orcid)0000-0001-7673-7728 aut Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Islamic Azad University (IAU) 2021 Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. Building performance (dpeaa)DE-He213 Daylight availability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Parametric design (dpeaa)DE-He213 Fadzil, S. F. S. aut Onubi, H. O. (orcid)0000-0001-5353-1276 aut BenLasod, S. A. aut Enthalten in International journal of environmental science and technology Tehran : Islamic Azad University, 2004 19(2021), 4 vom: 28. Apr., Seite 2659-2676 (DE-627)510463398 (DE-600)2230399-6 1735-2630 nnns volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676 https://dx.doi.org/10.1007/s13762-021-03328-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2021 4 28 04 2659-2676
allfieldsSound	10.1007/s13762-021-03328-9 doi (DE-627)SPR046552782 (SPR)s13762-021-03328-9-e DE-627 ger DE-627 rakwb eng Bahdad, A. A. S. verfasserin (orcid)0000-0001-7673-7728 aut Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Islamic Azad University (IAU) 2021 Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. Building performance (dpeaa)DE-He213 Daylight availability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Parametric design (dpeaa)DE-He213 Fadzil, S. F. S. aut Onubi, H. O. (orcid)0000-0001-5353-1276 aut BenLasod, S. A. aut Enthalten in International journal of environmental science and technology Tehran : Islamic Azad University, 2004 19(2021), 4 vom: 28. Apr., Seite 2659-2676 (DE-627)510463398 (DE-600)2230399-6 1735-2630 nnns volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676 https://dx.doi.org/10.1007/s13762-021-03328-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2021 4 28 04 2659-2676
language	English
source	Enthalten in International journal of environmental science and technology 19(2021), 4 vom: 28. Apr., Seite 2659-2676 volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676
sourceStr	Enthalten in International journal of environmental science and technology 19(2021), 4 vom: 28. Apr., Seite 2659-2676 volume:19 year:2021 number:4 day:28 month:04 pages:2659-2676
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Building performance Daylight availability Genetic algorithm Multi-objective optimization Parametric design
isfreeaccess_bool	false
container_title	International journal of environmental science and technology
authorswithroles_txt_mv	Bahdad, A. A. S. @@aut@@ Fadzil, S. F. S. @@aut@@ Onubi, H. O. @@aut@@ BenLasod, S. A. @@aut@@
publishDateDaySort_date	2021-04-28T00:00:00Z
hierarchy_top_id	510463398
id	SPR046552782
language_de	englisch
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author	Bahdad, A. A. S.
spellingShingle	Bahdad, A. A. S. misc Building performance misc Daylight availability misc Genetic algorithm misc Multi-objective optimization misc Parametric design Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency
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topic_title	Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency Building performance (dpeaa)DE-He213 Daylight availability (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Parametric design (dpeaa)DE-He213
topic	misc Building performance misc Daylight availability misc Genetic algorithm misc Multi-objective optimization misc Parametric design
topic_unstemmed	misc Building performance misc Daylight availability misc Genetic algorithm misc Multi-objective optimization misc Parametric design
topic_browse	misc Building performance misc Daylight availability misc Genetic algorithm misc Multi-objective optimization misc Parametric design
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title	Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency
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title_full	Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency
author_sort	Bahdad, A. A. S.
journal	International journal of environmental science and technology
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author_browse	Bahdad, A. A. S. Fadzil, S. F. S. Onubi, H. O. BenLasod, S. A.
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title_sort	multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency
title_auth	Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency
abstract	Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. © Islamic Azad University (IAU) 2021
abstractGer	Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. © Islamic Azad University (IAU) 2021
abstract_unstemmed	Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model. © Islamic Azad University (IAU) 2021
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title_short	Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency
url	https://dx.doi.org/10.1007/s13762-021-03328-9
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author2	Fadzil, S. F. S. Onubi, H. O. BenLasod, S. A.
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doi_str	10.1007/s13762-021-03328-9
up_date	2024-07-03T23:13:04.088Z
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S.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-7673-7728</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Islamic Azad University (IAU) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The greatest role in the building design is to optimize the use of natural daylight harvests to ensure human comfort and energy usage. This research aims to introduce an optimized office workspace that meets both daylight availability and energy efficiency. Honeybee and Ladybug plugins for grasshopper parametric software is utilized to simulate daylight and energy where multi-objective genetic optimization using non-dominated sorting genetic algorithms method is explored with octopus plugin, which is able to provide the best overall solution as a trade-off for multiple and conflicting design objectives simultaneously. The optimization focused on single-objective which shows major differences between daylight availability and energy efficiency while optimization for multi-objective together proved to be an efficient tool to research the trade-offs between the two contradictory objectives. The final best optimum balanced solutions can improve the $ sDA_{300/50%} $ by decreasing with an average of −11.88%, −2.34%, −5.94%, and −20.78%, while the $ UDI_{300-2000 lx} $ increased with an average by 39.39%, 29.23%, 46.17%, and 19.40%. The energy efficiency is slightly increased by + 1.33%, −0.61%, + 1.33%, and + 1.29%, in March, June, September, and December, respectively, compared to the reference model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Building performance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Daylight availability</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-objective optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parametric design</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fadzil, S. 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Multi-dimensions optimization for optimum modifications of light-shelves parameters for daylighting and energy efficiency

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