Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation

Daylight harvesting is a well-known strategy to address building energy efficiency. However, few simplified tools can evaluate its dual impact on lighting and air conditioning energy consumption. Artificial neural networks (ANNs) have been used as metamodels to predict energy consumption with high p...

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Autores principales:	Raphaela Walger da Fonseca, Fernando Oscar Ruttkay Pereira
Formato:	article
Lenguaje:	EN
Publicado:	SolarLits 2021
Materias:	daylight harvesting energy consumption metamodels artificial neural networks Details in building design and construction. Including walls, roofs TH2025-3000
Acceso en línea:	https://doaj.org/article/e5cfcd2176074507b5b5a1fa17a7e9fb
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spelling	oai:doaj.org-article:e5cfcd2176074507b5b5a1fa17a7e9fb2021-11-06T15:27:13ZMetamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation10.15627/jd.2021.202383-8701https://doaj.org/article/e5cfcd2176074507b5b5a1fa17a7e9fb2021-11-01T00:00:00Zhttps://solarlits.com/jd/8-255https://doaj.org/toc/2383-8701Daylight harvesting is a well-known strategy to address building energy efficiency. However, few simplified tools can evaluate its dual impact on lighting and air conditioning energy consumption. Artificial neural networks (ANNs) have been used as metamodels to predict energy consumption with high precision, few input parameters and instant response. However, this approach still lacks the potential to estimate consumption when there is daylight harvesting, at the ambient level, where the effect of orientation can be noted. This study investigates this potential, in order to evaluate the applicability of ANNs as a tool to aid the architectonic design. The ANNs were approached as metamodels trained based on EnergyPlus thermo-energetic simulations. The network configuration focused on determining its simplest feasible form. The input parameters adopted as the main variables of the building envelope were as follows: orientation, window-to-wall ratio and visible transmission. The effects of the encoding of orientation as a network input parameter, the number of examples of each variable for network training and changing the parameters used for the training were evaluated. The networks predicted the individualized consumption according to the end use with errors below 5%, indicating their potential to be applied as a simplified tool to support the design process, considering the elementary variables of the building envelope. The discussion of results focused on guidelines and challenges to achieve this purpose when contemplating the broadening of the metamodel scope.Raphaela Walger da FonsecaFernando Oscar Ruttkay PereiraSolarLitsarticledaylight harvestingenergy consumptionmetamodelsartificial neural networksDetails in building design and construction. Including walls, roofsTH2025-3000ENJournal of Daylighting, Vol 8, Iss 2, Pp 255-269 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	daylight harvesting energy consumption metamodels artificial neural networks Details in building design and construction. Including walls, roofs TH2025-3000
spellingShingle	daylight harvesting energy consumption metamodels artificial neural networks Details in building design and construction. Including walls, roofs TH2025-3000 Raphaela Walger da Fonseca Fernando Oscar Ruttkay Pereira Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation
description	Daylight harvesting is a well-known strategy to address building energy efficiency. However, few simplified tools can evaluate its dual impact on lighting and air conditioning energy consumption. Artificial neural networks (ANNs) have been used as metamodels to predict energy consumption with high precision, few input parameters and instant response. However, this approach still lacks the potential to estimate consumption when there is daylight harvesting, at the ambient level, where the effect of orientation can be noted. This study investigates this potential, in order to evaluate the applicability of ANNs as a tool to aid the architectonic design. The ANNs were approached as metamodels trained based on EnergyPlus thermo-energetic simulations. The network configuration focused on determining its simplest feasible form. The input parameters adopted as the main variables of the building envelope were as follows: orientation, window-to-wall ratio and visible transmission. The effects of the encoding of orientation as a network input parameter, the number of examples of each variable for network training and changing the parameters used for the training were evaluated. The networks predicted the individualized consumption according to the end use with errors below 5%, indicating their potential to be applied as a simplified tool to support the design process, considering the elementary variables of the building envelope. The discussion of results focused on guidelines and challenges to achieve this purpose when contemplating the broadening of the metamodel scope.
format	article
author	Raphaela Walger da Fonseca Fernando Oscar Ruttkay Pereira
author_facet	Raphaela Walger da Fonseca Fernando Oscar Ruttkay Pereira
author_sort	Raphaela Walger da Fonseca
title	Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation
title_short	Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation
title_full	Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation
title_fullStr	Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation
title_full_unstemmed	Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation
title_sort	metamodeling of the energy consumption of buildings with daylight harvesting – application of artificial neural networks sensitive to orientation
publisher	SolarLits
publishDate	2021
url	https://doaj.org/article/e5cfcd2176074507b5b5a1fa17a7e9fb
work_keys_str_mv	AT raphaelawalgerdafonseca metamodelingoftheenergyconsumptionofbuildingswithdaylightharvestingapplicationofartificialneuralnetworkssensitivetoorientation AT fernandooscarruttkaypereira metamodelingoftheenergyconsumptionofbuildingswithdaylightharvestingapplicationofartificialneuralnetworkssensitivetoorientation
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Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation

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