Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization

Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method a...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chi, Doris A [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms

Übergeordnetes Werk:	Enthalten in: Solar energy - Amsterdam [u.a.] : Elsevier Science, 1957, 234, Seite 304-318
Übergeordnetes Werk:	volume:234 ; pages:304-318

DOI / URN:	10.1016/j.solener.2022.01.068

Katalog-ID:	ELV007458681

Internformat


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245	1	0	\|a Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization
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520			\|a Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS.
650		4	\|a Solar energy density
650		4	\|a Daylight availability
650		4	\|a Total annual energy
650		4	\|a Façade orientation
650		4	\|a Multi-factor statistical analysis
650		4	\|a Evolutionary algorithms
773	0	8	\|i Enthalten in \|t Solar energy \|d Amsterdam [u.a.] : Elsevier Science, 1957 \|g 234, Seite 304-318 \|h Online-Ressource \|w (DE-627)320525597 \|w (DE-600)2015126-3 \|w (DE-576)096806648 \|x 1471-1257 \|7 nnns
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912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 52.56 \|j Regenerative Energieformen \|j alternative Energieformen
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Indexfelder

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publishDate	2022
allfields	10.1016/j.solener.2022.01.068 doi (DE-627)ELV007458681 (ELSEVIER)S0038-092X(22)00079-2 DE-627 ger DE-627 rda eng 530 DE-600 52.56 bkl Chi, Doris A verfasserin aut Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS. Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms Enthalten in Solar energy Amsterdam [u.a.] : Elsevier Science, 1957 234, Seite 304-318 Online-Ressource (DE-627)320525597 (DE-600)2015126-3 (DE-576)096806648 1471-1257 nnns volume:234 pages:304-318 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.56 Regenerative Energieformen alternative Energieformen AR 234 304-318
spelling	10.1016/j.solener.2022.01.068 doi (DE-627)ELV007458681 (ELSEVIER)S0038-092X(22)00079-2 DE-627 ger DE-627 rda eng 530 DE-600 52.56 bkl Chi, Doris A verfasserin aut Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS. Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms Enthalten in Solar energy Amsterdam [u.a.] : Elsevier Science, 1957 234, Seite 304-318 Online-Ressource (DE-627)320525597 (DE-600)2015126-3 (DE-576)096806648 1471-1257 nnns volume:234 pages:304-318 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.56 Regenerative Energieformen alternative Energieformen AR 234 304-318
allfields_unstemmed	10.1016/j.solener.2022.01.068 doi (DE-627)ELV007458681 (ELSEVIER)S0038-092X(22)00079-2 DE-627 ger DE-627 rda eng 530 DE-600 52.56 bkl Chi, Doris A verfasserin aut Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS. Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms Enthalten in Solar energy Amsterdam [u.a.] : Elsevier Science, 1957 234, Seite 304-318 Online-Ressource (DE-627)320525597 (DE-600)2015126-3 (DE-576)096806648 1471-1257 nnns volume:234 pages:304-318 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.56 Regenerative Energieformen alternative Energieformen AR 234 304-318
allfieldsGer	10.1016/j.solener.2022.01.068 doi (DE-627)ELV007458681 (ELSEVIER)S0038-092X(22)00079-2 DE-627 ger DE-627 rda eng 530 DE-600 52.56 bkl Chi, Doris A verfasserin aut Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS. Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms Enthalten in Solar energy Amsterdam [u.a.] : Elsevier Science, 1957 234, Seite 304-318 Online-Ressource (DE-627)320525597 (DE-600)2015126-3 (DE-576)096806648 1471-1257 nnns volume:234 pages:304-318 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.56 Regenerative Energieformen alternative Energieformen AR 234 304-318
allfieldsSound	10.1016/j.solener.2022.01.068 doi (DE-627)ELV007458681 (ELSEVIER)S0038-092X(22)00079-2 DE-627 ger DE-627 rda eng 530 DE-600 52.56 bkl Chi, Doris A verfasserin aut Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS. Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms Enthalten in Solar energy Amsterdam [u.a.] : Elsevier Science, 1957 234, Seite 304-318 Online-Ressource (DE-627)320525597 (DE-600)2015126-3 (DE-576)096806648 1471-1257 nnns volume:234 pages:304-318 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.56 Regenerative Energieformen alternative Energieformen AR 234 304-318
language	English
source	Enthalten in Solar energy 234, Seite 304-318 volume:234 pages:304-318
sourceStr	Enthalten in Solar energy 234, Seite 304-318 volume:234 pages:304-318
format_phy_str_mv	Article
bklname	Regenerative Energieformen alternative Energieformen
institution	findex.gbv.de
topic_facet	Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms
dewey-raw	530
isfreeaccess_bool	false
container_title	Solar energy
authorswithroles_txt_mv	Chi, Doris A @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320525597
dewey-sort	3530
id	ELV007458681
language_de	englisch
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author	Chi, Doris A
spellingShingle	Chi, Doris A ddc 530 bkl 52.56 misc Solar energy density misc Daylight availability misc Total annual energy misc Façade orientation misc Multi-factor statistical analysis misc Evolutionary algorithms Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization
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topic_title	530 DE-600 52.56 bkl Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization Solar energy density Daylight availability Total annual energy Façade orientation Multi-factor statistical analysis Evolutionary algorithms
topic	ddc 530 bkl 52.56 misc Solar energy density misc Daylight availability misc Total annual energy misc Façade orientation misc Multi-factor statistical analysis misc Evolutionary algorithms
topic_unstemmed	ddc 530 bkl 52.56 misc Solar energy density misc Daylight availability misc Total annual energy misc Façade orientation misc Multi-factor statistical analysis misc Evolutionary algorithms
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title	Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization
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title_full	Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization
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title_sort	solar energy density as a benchmark to improve daylight availability and energy performance in buildings: a single metric for a single-objective optimization
title_auth	Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization
abstract	Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS.
abstractGer	Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS.
abstract_unstemmed	Different simulation engines are currently required to perform daylight and energy evaluations of complex fenestration systems: Radiance has been validated to evaluate complex geometries but EnergyPlus cannot deal with them, making infeasible the thermal evaluations. The Shading Coefficient method adds an extra step to overcome that limitation: it runs irradiance simulations to create shading schedules that are shared between the simulation engines. This work supports the premise that Solar Energy Density (SED) can be used as a benchmark to evaluate the dual performance of perforated screens (PS). Therefore, only irradiance calculations will be required in evaluations – instead of running three different simulations. Orthogonal Arrays and Principal Component Analysis were the statistical techniques used to select the PS sample and weight the simulation results. The resultant SED thresholds concurrently fostered the accomplishment of daylighting and energy goals, at five different orientations: they maximised the daylit area and minimised the overlit area and total energy use. This work also presents an application example of the single-metric approach to test its effectiveness to perform single-objective optimization of PS design by using Evolutionary Algorithms. The SED approach has the advantage of reducing considerably the simulation time needed to perform the parametric optimization of PS.
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title_short	Solar energy density as a benchmark to improve daylight availability and energy performance in buildings: A single metric for a single-objective optimization
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up_date	2024-07-07T00:48:56.058Z
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