Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages

Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and provid...

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Autores principales:	Hanieh Nourkojouri, Nastaran Seyed Shafavi, Mohammad Tahsildoost, Zahra Sadat Zomorodian
Formato:	article
Lenguaje:	EN
Publicado:	SolarLits 2021
Materias:	artificial neural networks machine learning daylight assessment visual comfort Details in building design and construction. Including walls, roofs TH2025-3000
Acceso en línea:	https://doaj.org/article/be97f02be2704d5aa130154e4b7a94ee
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spelling	oai:doaj.org-article:be97f02be2704d5aa130154e4b7a94ee2021-11-29T12:08:09ZDevelopment of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages10.15627/jd.2021.212383-8701https://doaj.org/article/be97f02be2704d5aa130154e4b7a94ee2021-11-01T00:00:00Zhttps://solarlits.com/jd/8-270https://doaj.org/toc/2383-8701Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and providing a framework for the required analyses. A dataset was primarily derived from 2880 simulations developed from Honeybee for Grasshopper. The simulations were conducted for a side-lit shoebox model. The alternatives emerged from different physical features, including room dimensions, interior surfaces’ reflectance factor, window dimensions, room orientations, number of windows, and shading states. Five metrics were applied for daylight evaluations, including useful daylight illuminance, spatial daylight autonomy, mean daylight autonomy, annual sunlit exposure, and spatial visual discomfort. Moreover, view quality was analyzed via a grasshopper-based algorithm, developed from the LEED v4 evaluation framework. The dataset was further analyzed with an artificial neural network algorithm. The proposed predictive model had an architecture with a single hidden layer consisting of 40 neurons. The predictive model learns through a trial and error method with the aid of loss functions of mean absolute error and mean square error. The model was further analyzed with a new set of data for the validation process. The accuracy of the predictions was estimated at 97% on average. The View range metric in the quality view assessment, mean daylight autonomy and useful daylight illuminance had the best prediction accuracy among others respectively. The developed model which is presented as a framework could be used in early design stage analyses without the requirement of time-consuming simulations.Hanieh NourkojouriNastaran Seyed ShafaviMohammad TahsildoostZahra Sadat ZomorodianSolarLitsarticleartificial neural networksmachine learningdaylight assessmentvisual comfortDetails in building design and construction. Including walls, roofsTH2025-3000ENJournal of Daylighting, Vol 8, Iss 2, Pp 270-283 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks machine learning daylight assessment visual comfort Details in building design and construction. Including walls, roofs TH2025-3000
spellingShingle	artificial neural networks machine learning daylight assessment visual comfort Details in building design and construction. Including walls, roofs TH2025-3000 Hanieh Nourkojouri Nastaran Seyed Shafavi Mohammad Tahsildoost Zahra Sadat Zomorodian Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages
description	Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and providing a framework for the required analyses. A dataset was primarily derived from 2880 simulations developed from Honeybee for Grasshopper. The simulations were conducted for a side-lit shoebox model. The alternatives emerged from different physical features, including room dimensions, interior surfaces’ reflectance factor, window dimensions, room orientations, number of windows, and shading states. Five metrics were applied for daylight evaluations, including useful daylight illuminance, spatial daylight autonomy, mean daylight autonomy, annual sunlit exposure, and spatial visual discomfort. Moreover, view quality was analyzed via a grasshopper-based algorithm, developed from the LEED v4 evaluation framework. The dataset was further analyzed with an artificial neural network algorithm. The proposed predictive model had an architecture with a single hidden layer consisting of 40 neurons. The predictive model learns through a trial and error method with the aid of loss functions of mean absolute error and mean square error. The model was further analyzed with a new set of data for the validation process. The accuracy of the predictions was estimated at 97% on average. The View range metric in the quality view assessment, mean daylight autonomy and useful daylight illuminance had the best prediction accuracy among others respectively. The developed model which is presented as a framework could be used in early design stage analyses without the requirement of time-consuming simulations.
format	article
author	Hanieh Nourkojouri Nastaran Seyed Shafavi Mohammad Tahsildoost Zahra Sadat Zomorodian
author_facet	Hanieh Nourkojouri Nastaran Seyed Shafavi Mohammad Tahsildoost Zahra Sadat Zomorodian
author_sort	Hanieh Nourkojouri
title	Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages
title_short	Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages
title_full	Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages
title_fullStr	Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages
title_full_unstemmed	Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages
title_sort	development of a machine-learning framework for overall daylight and visual comfort assessment in early design stages
publisher	SolarLits
publishDate	2021
url	https://doaj.org/article/be97f02be2704d5aa130154e4b7a94ee
work_keys_str_mv	AT haniehnourkojouri developmentofamachinelearningframeworkforoveralldaylightandvisualcomfortassessmentinearlydesignstages AT nastaranseyedshafavi developmentofamachinelearningframeworkforoveralldaylightandvisualcomfortassessmentinearlydesignstages AT mohammadtahsildoost developmentofamachinelearningframeworkforoveralldaylightandvisualcomfortassessmentinearlydesignstages AT zahrasadatzomorodian developmentofamachinelearningframeworkforoveralldaylightandvisualcomfortassessmentinearlydesignstages
_version_	1718407425282801664

Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages

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