Abstract
Research in daylighting and electric lighting focuses on the physical quantity of light, physiological response to the physical stimuli, and the resulting visual perception. This chapter starts with an overview of the measurement and simulation-based research approaches used in understanding and quantifying the lighting availability and variability in the luminous environment. Field and laboratory measurements are discussed along with computational techniques. The rest of the chapter focuses on psychophysical research methods that aim to quantify the human physiological and psychological responses to the quantity and distribution of light.
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References
Allan, A. C., Garcia-Hansen, V., Isoardi, G., & Smith, S. S. (2019). Subjective assessments of lighting quality: A measurement review. Leukos, 15(2–3), 115–126.
Amundadottir, M. L., Lockley, S. W., & Andersen, M. (2017). Unified framework to evaluate non-visual spectral effectiveness of light for human health. Lighting Research and Technology, 49, 673–696.
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., & Shettle, E. P. (1986). Air force geophysical laboratory atmospheric constituent profiles. Hanscom: Air Force Geophysics Lab.
Ayoub, M. (2020). A review on machine learning algorithms to predict daylighting inside buildings. Solar Energy, 202, 249–275.
Balakrishnan, P., & Jakubiec, A. (2016). Measuring light through trees for daylight simulations: A photographic and photometric method. Proceedings of Building Simulation and Optimization, Newcastle, UK.
Bargary, G., Furlan, M., Raynham, P., Barbur, J., & Smith, A. T. (2015). Cortical hyperexcitability and sensitivity to discomfort glare. Neuropsychologia, 69, 194–200.
Berman, S. M., Bullimore, M. A., Jacobs, R. J., Bailey, I. L., & Gandhi, N. (1994). An objective measure of discomfort glare. Journal of the Illuminating Engineering Society, 23(2), 40–49.
Berman, S. M., Bullimore, M. A., Bailey, I. L., & Jacobs, R. J. (1996). The influence of spectral composition on discomfort glare for large-size sources. Journal of the Illuminating Engineering Society, 25(1), 34–41.
Bertenshaw, D. R. (2020). The standardisation of light and photometry – A historical review. Lighting Research and Technology, 0, 1–33.
Bierman, A., Klein, T. R., & Rea, M. S. (2005). The Daysimeter: A device for measuring optical radiation as a stimulus for the human circadian system. Measurement Science and Technology, 16, 2292–2299.
Blackwell, R. (1959). Development and use of a quantitative method for specification of interior illumination levels on the basis of performance data. Illuminating Engineering, 54, 317–353.
Boyce, P. R. (1973). Age, illuminance, visual performance and preference. Lighting Research and Technology, 5, 125–140.
Boyce, P. R. (2014). Human factors in lighting. London: Taylor and Francis Group.
Boyce, P. R., Veitch, J. A., & Newsham, G. R. (2006). Lighting quality and office work: two field simulation experiments. Lighting Research and Technology, 38(3), 191–223.
Brainard, G. C., Hanifin, J. R., Greeson, J. M., Byrne, B., Glickman, G., Gerner, E., & Rollag, M. D. (2001). Action spectrum for melatonin regulation in humans: Evidence for a novel circadian photoreceptor. Journal of Neuroscience, 21(16), 6405–6412.
Cadik, M., Wimmer, M., Neumann, L., & Artusi, A. (2008). Evaluation of HDR tone mapping methods using essential perceptual attributes. Computer and Graphics, 32(3), 330–349.
Chamilothori, K., Wienold, J., & Andersen, M. (2019). Adequacy of immersive virtual reality for the perception of daylit spaces: Comparison of real and virtual environments. Leukos, 15(2-3), 203–226.
Chauvel, P., Collins, J. B., Dogniaux, R., & Longmore, J. (1982). Glare from windows: Current views of the problem. Lighting Research and Technology, 14(1), 31–46.
CIE. (1987). Methods of characterizing illuminance meters and luminance meters: Performance, characteristics, and specification, Vienna, Austria.
CIE. (1996). Spatial distribution of daylight - CIE standard overcast sky and clear sky. ISO 15469/CIE S003, Vienna, Austria.
CIE. (2006). CIE 171 - Test cases to assess the accuracy of lighting computer programs. Vienna, Austria.
CIE. (2018). CIE system for metrology of optical radiation fir ipRGC-influenced responses to light. CIE S 026/E:2018, Vienna, Austria.
Debevec, P. (2002). Image-based Lighting. IEEE Computer Graphics and Applications, March/April, pp. 26–34.
Debevec, P. E., & Malik, J. (1997). Recovering high dynamic range radiance maps from photographs. Proceedings of ACM SIGGRAPH, 3–8 August, Los Angeles, pp. 369–378.
DiLaura, D. L., Houser, K. W., Mistrick, R. G., & Steffy, G. R. (2011). The lighting handbook. New York: Illuminating Engineering Society.
Eklund, N. H., & Boyce, P. R. (1996). The development of a reliable, valid, and simple office lighting survey. Journal of the Illuminating Engineering Society, 25(2), 25–40.
EN: European Committee for Standardization. (2018). Daylight in buildings. Brussels: EN 17037.
Enezi, J. A., Revell, V., Brown, T., Wynne, J., Schlangen, L., & Lucas, R. (2011). A melanopic spectral efficiency function predicts the sensitivity of melanopsin photoreceptors to polychromatic lights. Journal of Biological Rhythms, 26(4), 314–323.
Figueiro, M. G., Brainard, G. C., Lockley, S. W., Revell, V. L., White, R. (2008). Light and human health: An overview of the impact of optical radiation on visual, circadian, neuroendocrine, and neurobehavioral responses, IES TM-18-08. New York: Illuminating Engineering Society.
Flynn, J. E., Hendrick, C., Spencer, T., & Martyniuk, O. (1979). A guide to methodology procedures for measuring subjective impressions in lighting. Journal of the Illuminating Engineering Society, 8(2), 95–110.
Gescheider, G. (1984). Psychophysics: Method, theory, and application. Hillsdale: Lawrence Erlbaum.
Grobe, L. O. (2019). Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation, 12(6), 745–758.
Hernandez-Andres, J., Romero, J., & Nieves, J. L. (2001). Color and spectral analysis of daylight in Southern Europe. Journal of Optical Society of America, 18(6), 1325–1335.
Hopkinson, R. G. (1972). Glare from daylighting in buildings. Applied Ergonomics, 3(4), 206–215.
Ibarra, D., & Reinhart, C. F. (2013). Teaching daylight simulations – Improving modeling workflows for simulation novices. Proceedings of Building Simulation, Chambery, France.
IBPSA. (2020). Building Energy Software Tools (BEST) Directory, International Building Performance Simulation Association, https://www.buildingenergysoftwaretools.com/
IEA. (1999). Post occupancy evaluation of daylight in buildings. Galve: International Energy Agency.
IEA. (2016). Monitoring protocol for lighting and daylighting retrofits. Stuttgart: International Energy Agency.
IES. (2012). IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). Illuminating Engineering Society, New York: LM-83-12.
IES. (2013). Recommended Practice for Daylighting Buildings, Illuminating Engineering Society, New York: IES RP-5-13.
IGDB: International Glazing Database. (2020). Lawrence Berkeley National Laboratory, https://windows.lbl.gov/software/igdb
Inanici, M. (2006). Evaluation of high dynamic range photography as a luminance data acquisition system. Lighting Research and Technology, 38(2), 123–136.
Inanici, M. (2010). Evaluation of high dynamic range image-based sky models in lighting simulation. Leukos, 7(2), 69–84.
Inanici, M. (2019). Tri-stimulus color accuracy in image-based sky models: Simulating the impact of color distributions throughout the sky-dome on daylit interiors with different orientations, Proceedings of Building Simulation, Rome, Italy.
Inanici, M., & Hashemloo, A. (2017). An investigation of the daylighting simulation techniques and sky modeling practices for occupant centric evaluations. Building and Environment, 113, 220–231.
Inanici, M., Brennan, M., & Clark, E. (2015). Spectral lighting simulations: Computing circadian light. Proceedings of Building Simulation, Hyderabad, India, pp. 1245–1252.
Jakubiec A, van den Wymelenberg K, Inanici M, Mahic A. (2016a). Accurate measurement of daylit interior scenes using high dynamic range photography. Proceedings of CIE Lighting Quality and Energy Efficiency Conference, Melbourne, Australia.
Jakubiec, A., Inanici, M., van den Wymelenberg, K., & Mahic, A. (2016b). Improving the accuracy of measurements in daylit interior scenes using high dynamic range photography. Proceedings of Passive and Low Energy Architecture Conference, Los Angeles, CA.
Jones, N., & Reinhart, C. F. (2017). Experimental validation of ray tracing as a means of image-based visual discomfort prediction. Building and Environment, 113, 131–150.
Jung, B. Y., & Inanici, M. (2019). Measuring circadian lighting through high dynamic range photography. Lighting Research and Technology, 51(5), 742–763.
Kelly, K. (2017). A different type of lighting research – A qualitative methodology. Lighting Research and Technology, 49(8), 933–942.
Knoop, M., Diakite, A., & Rudawski, F. (2015). Methodology to create spectral sky models to enable the inclusion of colorimetric characteristics of daylight in research and design. Proceedings from CIE Conference, Manchaster, UK.
Kort, Y. (2019). Tutorial: Theoretical considerations when planning research on human factors in lighting. Leukos, 15(2-3), 85–96.
Liu, Y., Colburn, A., & Inanici, M. (2018). Computing long-term daylighting simulations from high dynamic range imagery using deep neural networks, Proceedings of SimBuild (co-organized by ASHRAE and IBPSA-USA), Chicago IL.
Liu, Y., Colburn, A., & Inanici, M. (2020). Deep neural network approach for annual luminance simulations. Journal of Building Performance Simulation. 13(5), 532–554.
Lucas, R., Peirson, S., Berson, D., Brown, T., Cooper, H., Czeisler, C., Figuero, M., Gamlin, P., Lockley, S., O’Hagan, J., Price, L., Provencio, I., Skene, D., & Brainard, G. (2014). Measuring and using light in the melanopsin age. Trends in Neurosciences, 37(1), 1–9.
Mardaljevic, J. (1995). Validation of a lighting simulation program under real sky conditions. Lighting Research and Technology, 27(4), 181–188.
Mardaljevic, J. (2001). The BRE-IDMP dataset: A new benchmark for the validation of illuminance prediction techniques. Lighting Research and Technology, 33(2), 117–134.
Mardaljevic, J., Andersen, M., Roy, N., & Christoffersen, J. (2013). A framework for predicting the non-visual effects of daylight – Part II: The simulation model. Lighting Research and Technology, 46(4), 388–406.
McNeil, A., & Lee, E. S. (2013). A validation of the radiance three-phase simulation method for modelling annual daylight performance of optically complex fenestration systems. Journal of Building Performance Simulation, 6(1), 24–37.
Moroder, D. G., Lee, E. S., & Ward, G. (2017). Validation of the five-phase method for simulating complex fenestration systems with radiance against field measurements. Proceedings of Building Simulation, San Francisco, CA.
Nabil, A., & Mardaljevic, J. (2005). Useful daylight illuminance: A new paradigm for assessing daylight in buildings. Lighting Research and Technology, 37, 41–59.
Parsaee, M., Demers, C. M. H., Lalonde, J. H., Potvin, A., Inanici, M., & Hébert, M. (2020). Human-centric lighting performance of shading panels in architecture: A benchmarking study with lab scale physical models under real skies. Solar Energy, 204, 354–368.
Pechacek, C. S., Andersen, M., & Lockley, S. W. (2008). Preliminary method for prospective analysis of the circadian efficacy of (day)light with applications to healthcase architecture. Leukos, 5(1), 1–26.
Perez, R., Seals, J. M., & Ineichen, P. (1993). An all-weather model for sky luminance distribution. Solar Energy, 50(3), 235–245.
Rea, M. S., & Ouellette, M. J. (1991). Relative visual performance: A basis for application. Lighting Research and Technology, 23(3), 135–144.
Rea, M. S., Figueiro, M. G., Bierman, A., & Bullough, J. D. (2010). Circadian light. Journal of Circadian Rhythms, 8(2), 1–10.
Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., & Myszkowski, K. (2010). High dynamic range imaging. San Francisco: Morgan Kaufmann.
Reinhart, C. F. (2001). Daysim. https://github.com/MITSustainableDesignLab/Daysim
Reinhart, C. F. (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77(1), 15–28.
Reinhart, C. F. (2019). Daylighting performance predictions. In H. JLM & R. Lamberts (Eds.), Building performance and simulation for design and operation (pp. 221–269). New York: Routledge.
Reinhart, C. F., & Fitz, A. (2006). Findings from a survey on the current use of daylight simulations in building design. Energy and Buildings, 38(7), 824–835.
Reinhart, C. F., & Walkenhorst, O. (2001). Dynamic radiance-based daylight simulations for a full-scale test office with outer venetian blinds. Energy and Buildings, 33(7), 683–697.
Reinhart, C. F., Mardaljevic, J., & Rogers, Z. (2006). Dynamic daylight performance metrics for sustainable building design. Leukos, 3(1), 7–31.
Rockcastle, S., Chamilothori, K., & Andersen, M. (2017). An experiment in virtual reality to measure daylight-driven interest in rendered architectural scenes, Proceedings of Building Simulation, San Francisco, CA.
Ruppertsberg, A. I., & Bloj, M. (2006). Rendering complex scenes for psychophysics using radiance: How accurate can you get? Journal of Optical Society of America, 23(4), 759–768.
Sarey-Khanie, M., Anderson, M., Hart, B. M., Stoll, J., & Einhouser, W. (2011). Integration of eye-tracking methods in visual comfort assessments. Proceedings of CISBAT: CleanTech for Sustainable Buildings - From Nano to Urban Scale, Lausanne, Switzerland.
Seetzen, L., Whitehead, L. A., & Ward, G. (2003). A high dynamic range display using low and high resolution modulators. Society for Information Display International Symposium, 34(1), 1450–1453.
Solemma. (2019). ALFA – Adaptive lighting for alertness. https://solemma.com/Alfa.html
Stromann-Andersen, J., & Sattrup, P. A. (2011). The urban canyon and building energy use: Urban density versus daylight and passive solar gains. Energy and Buildings, 43(8), 2011–2020.
Stumpfel, J., Jones, A., Wenger, A., & Debevec, P. (2004). Direct HDR Capture of the Sun and Sky, International Conference on Virtual Reality, Computer Graphics, Visualization and Interaction in Africa, Cape Town, South Africa.
Tai, N. C., & Inanici, M. (2012). Luminance Contrast as Depth Cue: Investigations and Design Applications. Journal of Computer-Aided Design and Applications, 9(5), 691–705.
Thapan, K., Arendt, J., & Skene, D. J. (2001). An action spectrum for melatonin suppression: evidence for a novel non-rod, non-cone photoreceptor system in humans. Journal of Physiology, 535(1), 261–267.
Tregenza, P. R., & Waters, I. M. (1983). Daylight coefficients. Lighting Research and Technology, 19(1), 65–71.
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(3), 145–164.
Veitch, J. A. (2001). Psychological processes influencing lighting quality. Journal of the Illuminating Engineering Society, 30(1), 124–140.
Veitch, J. A., & Davis, R. (2019). Lighting research today: The more things change, the more they stay the same. Leukos, 15(2–3), 77–83.
Veitch, J. A., & Newsham, G. R. (1996). Determinants of lighting quality II: Research and recommendations. Annual Convention of American Psychological Association, Toronto, Canada.
Veitch, J. A., Charles, K., Farley, K. M. J., & Newsham, G. R. (2007). A model of satisfaction with open-plan office conditions: COPE field findings. Journal of Environmental Psychology, 27(3), 177–189.
Veitch, J. A., Fatios, S. A., & Houser, K. W. (2019). Judging the scientific quality of applied lighting research. Journal of the Illuminating Engineering Society, 15(2-3), 97–114.
Wagdy, A., Garcia-Hansen, V., Elhenawy, M., Isoardi, G., Drogemuller, R., & Fathy, F.. (2020). Machine Learning Framework for developing glare predictive models, CIE Australia Lighting Research Conference, Brisbane, Australia.
Wang, T., Ward, G., & Lee, E. S. (2018). Efficient modeling of optically-complex, non-coplanar exterior shading: Validation of matrix algebraic methods. Energy and Buildings, 174, 464–483.
Ward, G. (1994). The radiance lighting simulation and rendering system (pp. 459–472). Orlando, FL: Proceedings of ACM SIGGRAPH.
Ward, G. (2005). Photosphere. http://www.anyhere.com/
Wienold, J. (2009). Dynamic daylight glare evaluation. Proceedings of Building Simulation, Glasgow, UK.
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(7), 743–757.
Wyszecki, G., & Stiles, W. S. (2000). Color science: Concepts and methods, quantitative data and formulae. New York: John Wiley.
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Inanici, M. (2021). Research Methods in Daylighting and Electric Lighting. In: Azari, R., Rashed-Ali, H. (eds) Research Methods in Building Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-73692-7_4
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