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The EBD-SIM (evidence-based design, simulation) framework is a conceptual framework developed to integrate the use of lighting simulation in the EBD process to provide a holistic performance evaluation method. A real-time case study, executed in a fully operational office building, is used to demonstrate the framework’s performance. The case study focused on visual comfort analysis. The objective is to demonstrate the applicability of the developed EBD-SIM framework using correlations between current visual comfort metrics and actual human perception as evaluation criteria. The data were collected via simulation for visual comfort analysis and via questionnaires for instantaneous and annual visual comfort perception. The study showed that for user perception, the most crucial factor for visual comfort is the amount of light on a task area, and simple metrics such as Eh-room and Eh-task had a higher correlation with perceived visual comfort than complex performance metrics such as Daylight Autonomy (DA). To improve the design process, the study suggests that, among other things, post-occupancy evaluations (POEs) should be conducted more frequently to obtain better insight into user perception of daylight and subsequently use new evidence to further improve the design of the EBD-SIM model.


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The use of lighting simulation in the evidence-based design process: A case study approach using visual comfort analysis in offices

Show Author's information Anahita Davoodi( )Peter JohanssonMyriam Aries
Department of Construction Engineering and Lighting Science, Jönköping University, Sweden

Abstract

The EBD-SIM (evidence-based design, simulation) framework is a conceptual framework developed to integrate the use of lighting simulation in the EBD process to provide a holistic performance evaluation method. A real-time case study, executed in a fully operational office building, is used to demonstrate the framework’s performance. The case study focused on visual comfort analysis. The objective is to demonstrate the applicability of the developed EBD-SIM framework using correlations between current visual comfort metrics and actual human perception as evaluation criteria. The data were collected via simulation for visual comfort analysis and via questionnaires for instantaneous and annual visual comfort perception. The study showed that for user perception, the most crucial factor for visual comfort is the amount of light on a task area, and simple metrics such as Eh-room and Eh-task had a higher correlation with perceived visual comfort than complex performance metrics such as Daylight Autonomy (DA). To improve the design process, the study suggests that, among other things, post-occupancy evaluations (POEs) should be conducted more frequently to obtain better insight into user perception of daylight and subsequently use new evidence to further improve the design of the EBD-SIM model.

Keywords: building performance simulation, visual comfort, lighting simulation, lighting quality, office field study, evidence-based design

References(49)

Bian Y, Luo T (2017). Investigation of visual comfort metrics from subjective responses in China: A study in offices with daylight. Building and Environment, 123: 661-671.
Bluyssen PM, Aries M, van Dommelen P (2011). Comfort of workers in office buildings: The European HOPE project. Building and Environment, 46: 280-288.
Burnham JF (2006). Scopus database: a review. Biomedical Digital Libraries, 3: 1.
Cannavale A, Fiorito F, Resta D, Gigli G (2013). Visual comfort assessment of smart photovoltachromic windows. Energy and Buildings, 65: 137-145.
Cannavale A, Hörantner M, Eperon GE, Snaith HJ, Fiorito F, Ayr U, Martellotta F (2017). Building integration of semitransparent perovskite-based solar cells: Energy performance and visual comfort assessment. Applied Energy, 194: 94-107.
Carlopio JR (1996). Construct validity of a physical work environment satisfaction questionnaire. Journal of Occupational Health Psychology, 1: 330-344.
Carlucci S, Causone F, de Rosa F, Pagliano L (2015). A review of indices for assessing visual comfort with a view to their use in optimization processes to support building integrated design. Renewable and Sustainable Energy Reviews, 47: 1016-1033.
Chatzikonstantinou I, Sariyildiz S (2016). Approximation of simulation- derived visual comfort indicators in office spaces: a comparative study in machine learning. Architectural Science Review, 59: 307-322.
Costanzo V, Donn M (2017). Thermal and visual comfort assessment of natural ventilated office buildings in Europe and North America. Energy and Buildings, 140: 210-223.
Cuttle C (2015). Lighting Design: A Perception-Based Approach. Abingdon, UK: Routledge.
DOI
Davoodi A (2016). Lighting simulation for a more value-driven building design process. Licentiate Thesis, Lund University, Sweden.
Davoodi A, Johansson P, Henricson M, Aries M (2017). A conceptual framework for integration of evidence-based design with lighting simulation tools. Buildings, 7: 82.
Dillon R Vischer JC (1987). Derivation of the Tenant Questionnaire Survey assessment method: Office building occupant survey data analysis, Public works Canada.
Dykes C, Baird G (2013). A review of questionnaire-based methods used for assessing and benchmarking indoor environmental quality. Intelligent Buildings International, 5: 135-149.
Goodman LA (1961). Snowball sampling. The Annals of Mathematical Statistics, 32: 148-170.
Heschong L, Wymelenberg VD, Andersen M, Digert N, Fernandes L, Keller A, Loveland J, McKay H, Mistrick R, Mosher B (2012). IES LM-83-12. Approved Method: IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE), IES-Illuminating Engineering Society.
DOI
Hien WN, Poh LK, Feriadi H (2000). The use of performance-based simulation tools for building design and evaluation—a Singapore perspective. Building and Environment, 35: 709-736.
Hygge S Lofberg H (1997). User evaluation of visual comfort in some buildings of the Daylight Europe Project. In: Proceedings of Right Light Four, the fourth European conference on Energy-Efficient Lighting, Copenhagen, Denmark, vol. 2, pp. 69-74.
DOI
Jakubiec JA Reinhart CF (2011). DIVA 2.0: integrating daylight and thermal simulations using Rhinoceros 3D, Daysim and Energyplus. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.
DOI
Jakubiec JA, Reinhart CF (2012). The “adaptive zone”—A concept for assessing discomfort glare throughout daylit spaces. Lighting Research & Technology, 44: 149-170.
Jakubiec JA, Reinhart CF, Van Den Wymelenberg K (2015). Towards an integrated framework for predicting visual comfort conditions from luminance-based metrics in perimeter daylit spaces. In: Proceedings of the 14th International IBPSA Building Simulation Conference, Hyderabad, India.
DOI
Kent MG, Fotios S, Altomonte S (2019a). An experimental study on the effect of visual tasks on discomfort due to peripheral glare. Leukos, 15: 17-28.
Kent MG, Fotios S, Altomonte S (2019b). Discomfort glare evaluation: The influence of anchor bias in luminance adjustments. Lighting Research & Technology, 51: 131-146.
Konis K, Lee E Clear R (2011). Visual comfort analysis of innovative interior and exterior shading systems for commercial buildings using high resolution luminance images. Leukos, 7: 167-188.
Konis K (2013). Evaluating daylighting effectiveness and occupant visual comfort in a side-lit open-plan office building in San Francisco, California. Building and Environment, 59: 662-677.
Linhart F, Scartezzini J-L (2011). Evening office lighting—Visual comfort vs. energy efficiency vs. performance? Building and Environment, 46: 981-989.
Malone E, Harmsen C, Reno K, Edelstein E, Hamilton D Salvatore A (2008). An introduction to evidence based design: Exploring healthcare and design. Concord, CA: The Center for Health Design.
DOI
Michael A, Heracleous C (2017). Assessment of natural lighting performance and visual comfort of educational architecture in Southern Europe: The case of typical educational school premises in Cyprus. Energy and Buildings, 140: 443-457.
Michael A, Gregoriou S, Kalogirou SA (2018). Environmental assessment of an integrated adaptive system for the improvement of indoor visual comfort of existing buildings. Renewable Energy, 115: 620-633.
Motamed A, Deschamps L Scartezzini J-L (2017). On-site monitoring and subjective comfort assessment of a sun shadings and electric lighting controller based on novel High Dynamic Range vision sensors. Energy and Buildings, 149: 58-72.
Nabil A, Mardaljevic J (2006). Useful daylight illuminances: A replacement for daylight factors. Energy and Buildings, 38: 905-913.
Newsham GR, Aries MBC, Mancini S, Faye G (2008). Individual control of electric lighting in a daylit space. Lighting Research & Technology, 40: 25-41.
Ochoa CE, Aries MBC, Hensen JLM (2012). State of the art in lighting simulation for building science: A literature review. Journal of Building Performance Simulation, 5: 209-233.
Peretti C Schiavon S (2011). Indoor environmental quality surveys. A brief literature review. In: Proceedings of the 12th International Conference on Indoor Air Quality and Climate, Austion, USA.
DOI
Reinhart C, Fitz A (2006). Findings from a survey on the current use of daylight simulations in building design. Energy and Buildings, 38: 824-835.
Reinhart CF, Mardaljevic J, Rogers Z (2006). Dynamic daylight perormance metrics for sustainable building design. Leukos, 3: 1-25.
Ruck N, Aschehoug O, Aydinli S, Christoffersen J, Edmonds I, Jakobiak R, Kischkoweit-Lopin M, Klinger M, Lee E, Courret G (2000). Daylight in buildings—A source book on daylighting systems and components. Lawrence Berkeley National Laboratory, Berkeley, USA.
Stokols D Scharf T (1990). Developing standardized tools for assessing employees’ ratings of facility performance. In: Davis G, Ventre F (eds), Performance of buildings and serviceability of facilities. Philadelphia, PA, USA: American Society for Testing and Materials.
USGBC (2008). LEED BD+C: New Construction | v4 - LEED v4: Daylight. Available at https://www.usgbc.org/credits/new-construction-commercial-interiors-schools-new-construction-retail-new-construction-ret-1.Accessed 27 Jan 2019.
Van Den Wymelenberg KG (2013). Evaluating human visual preference and performance in an office environment using luminance-based metrics. PhD Thesis, University of Washington, USA.
DOI
Van Den Wymelenberg K, Inanici M (2014). A critical investigation of common lighting design metrics for predicting human visual comfort in offices with daylight. Leukos, 10: 145-164.
Van Den Wymelenberg K, Inanici M (2016). Evaluating a new suite of luminance-based design metrics for predicting human visual comfort in offices with daylight. Leukos, 12: 113-138.
Vassiliades C, Michael A, Savvides A, Kalogirou S (2018). Improvement of passive behaviour of existing buildings through the integration of active solar energy systems. Energy, 163: 1178-1192.
Veitch JA, Charles KE, Farley KMJ, Newsham GR (2007). A model of satisfaction with open-plan office conditions: COPE field findings. Journal of Environmental Psychology, 27: 177-189.
Velds M Christofferesen J (2001). Monitoring procedures for the assessment of daylighting performance of buildings. IEA SHC Task 21 “Daylight in Buildings”/ECBCS Annex 29. International Energy Agency.
DOI
Vera S, Uribe D, Bustamante W, Molina G (2017). Optimization of a fixed exterior complex fenestration system considering visual comfort and energy performance criteria. Building and Environment, 113: 163-174.
Vischer JC (2009). Applying knowledge on building performance: From evidence to intelligence. Intelligent Buildings International, 1: 239-248.
Vischer JC Zeisel J (2008). Bridging the gap between research and design. World Health Design, 2008(July): 57-61.
Wienold J, Christoffersen J (2006). Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras. Energy and Buildings, 38: 743-757.
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Publication history

Received: 04 March 2019
Accepted: 27 August 2019
Published: 23 September 2019
Issue date: February 2020

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© The Author(s) 2019.

Acknowledgements

The authors would like to acknowledge the financial support of the Region Jönköpings Län’s FoU-fond Fastigheter and the Bertil and Britt Svenssons Stiftelse för Belysningsteknik. Also, we would like to acknowledge the valuable comments by the reviewers of the journal of Building Simulation and Professor Christine Räisänen for proofreading the manuscript.

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