Application of machine learning to predict daylight glare probability

[1] M.S. Barkhordari, M.S. Es-Haghi, Straightforward prediction for responses of the concrete shear wall buildings subject to ground motions using machine learning algorithms, Int. J. Eng. 34 (2021), no. 7, 1586–1601.

[2] J. Bromley and E. Sackinger, Neural-network and k-nearest-neighbor classifiers, AT&T Bell Laboratories, (1991), 11359–910819.

[3] R.D. Clear, Discomfort glare: What do we actually know?, Lighting Res. Techno. 45 (2013), 58–141.

[4] Commission Internationale de l’Eclairage CTC 3-13, Discomfort glare in interior lighting, Proc. CIE 21st Session, CIE Publication 117, Vienna: CIE, 1995.

[5] V. Derbentsev, V. Babenko, K. Khrustalev, H. Obruch and S. Khrustalova, Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices, Int. J. Eng. 34 (2021), no. 1, 140–148.

[6] H. Drucker, C.J. Burges, L. Kaufman, A. Smola, and V. Vapnik, Support vector regression machines, Adv. Neural Inf. Process. Syst. 9 (1997), 155–161.

[7] H.D. Einhorn, Discomfort glare: A formula to bridge differences, Lighting Res. Technol. 11 (1979), no. 2, 90–94.

[8] V.C. Handikherkar and V.M. Phalle, Gear fault detection using machine learning techniques-a simulation-driven approach, Int. J. Eng. 34 (2021), no. 1, 212–223.

[9] T. Hastie, R. Tibshirani, J.H. Friedman and J.H. Friedman, The elements of statistical learning: Data mining, inference, and prediction, Springer, New York, 2009.

[10] M.B. Hirning, The application of luminance mapping to discomfort glare: A modified glare index for green buildings, PhD diss., Queensland University of Technology, 2014.

[11] R.G. Hopkinson, Glare from daylighting in buildings, Appl. Ergon. 3 (1972), no. 4, 206–215.

[12] J.A. Jakubiec, Validation of simplified visual discomfort calculations, Build. Simul. Optim. Conf. (BSO2018), 2018, pp. 2–11.

[13] J.A. Jakubiec and C.F. Reinhart, DIVA 2.0: Integrating daylight and thermal simulations using rhinoceros 3D, DAYSIM and EnergyPlus, Proc. Build. Simul. 12th Conf. Int. Build. Perform Simul. Assoc. 20 (2011), no. 11, 2202–2209.

[14] J.A. Jakubiec and C. Reinhart, The ’adaptive zone’–A concept for assessing discomfort glare throughout daylit spaces, Lighting Res. Technol. 44 (2012), no. 2, 149–170.

[15] J.A. Jakubiec and C.F. Reinhart, A concept for predicting occupants’ long-term visual comfort within daylit spaces, LEUKOS–J. Illum. Eng. Soc. North America, 12 (2016), no. 4, 185–202.

[16] M.G. Kent, S. Altomonte, P.R. Tregenza and R. Wilson, Temporal variables and personal factors in glare sensation, Light. Res. Technol. 48 (2016), 710–689.

[17] M. Khedmati, F. Seifi and M.J. Azizi, Time series forecasting of Bitcoin price based on autoregressive integrated moving average and machine learning approaches, Int. J. Eng. 33 (2020), no. 7, 1293–1303.

[18] I. Konstantzos, A. Tzempelikos and Y.C. Chan, Experimental and simulation analysis of daylight glare probability in offices with dynamic window shades, Build. Environ. 87 (2015), 244–254.

[19] T. Kruisselbrink, R. Dangol and A. Rosemann, Photometric measurements of lighting quality: An overview, Build. Environ. 138 (2018), 42–52.

[20] S. Kumar and G. Sahoo, A random forest classifier based on genetic algorithm for cardiovascular diseases diagnosis (research note), Int. J. Eng. 30 (2017), no. 11, 1723–1729.

[21] D. Mah, M. Kim and A. Tzempelikos, Utilization of programmable cameras for web-based sensing and control of daylight in buildings, J. Phys.: Conf. Ser. 2042 (2021), no. 1, 012114.

[22] D.S. Maitra, U. Bhattacharya and S.K. Parui, CNN based common approach to handwritten character recognition of multiple scripts, 13th Int. Conf. Doc. Anal. Recog. (ICDAR), 2015, pp. 1021–1025.

[23] A. Mentens, S. Martin, F. Descamps, J. Lataire and V.A. Jacobs, Daylight glare probability prediction for an office room, Proc. CIE Midterm Meet., 2021.

[24] T.M. Mitchell, Machine Learning, McGraw-Hill, New York, 2007.

[25] M. Namakshenas, Real-time scheduling of a flexible manufacturing system using a two-phase machine learning algorithm, Int. J. Eng. 26 (2013), no. 9, 1067–1076.

[26] A. Nazzal, O. Guler and S. Onaygil, Subjective experience of discomfort glare in a daylit computerized office in Istanbul and its mathematical prediction with the DGIN method, ARI Bull. Istanbul Tech. Univer. 54 (2005), no. 03, 96–107.

[27] B. Painter, D. Fan and J. Mardaljevic, Evidence-based daylight research: Development of a new visual comfort monitoring method, 11th Int. Light. Conf., Istanbul, Turkey, 2009.

[28] C. Pierson and M. Bodart, Validation and universalization of daylight glare probability index, LumeNet 2016, Ghent, Belgium, 2016, 82.

[29] C. Reinhart and A. Fitz, Findings from a survey on the current use of daylight simulations in building design, Energy Build. 38 (2006), no. 7, 824–835.

[30] F. Rosenblatt, Principles of neurodynamics. perceptrons and the theory of brain mechanisms, Cornell Aeronautical Lab Inc Buffalo NY, 1961.

[31] J.A.Veitch, Light, lighting, and health: Issues for consideration, LEUKOS–J. Illumin. Eng. Soc. North Amer. 2 (2005), no. 2, 85–96.

[32] M. Piechowski and A. Rowe, Building design for hot and humid climates–implications on thermal comfort and energy efficiency, IBPSA 2007–Int. Build. Perform. Simul. Assoc., 2007, pp. 122–126.

[33] G. Ward, Rendering with radiance: The art and science of lighting visualization, Morgan Kaufman, 1998.

[34] J. Wienold and J. Christoffersen, Towards a new daylight glare rating, Lux Europa, Berlin, 2005, pp. 157–161.

[35] J. Wienold and J. Christoffersen, Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras, Energy Build. 38 (2006), no. 7, 743–757.

[36] J. Wienold and Fraunhofer Institute for Solar Energy Systems ISE, Daylight glare in offices, Fraunhofer Verlag, Alemanha, 2010.

[37] J. Wienold, T. Iwata, M. Sarey Khanie, E. Erell, E. Kaftan, R.G. Rodriguez, J.A. Yamin Garret´on, T. Tzempelikos, I. Konstantzos, J. Christoffersen and T.E. Kuhn, Cross-validation and robustness of daylight glare metrics, Light. Res. Technol. 51 (2019), no. 7, 983–1013.

[38] J.A. Yamin Garreton, R.G. Rodriguez, A. Ruiz and A.E. Pattini, Degree of eye-opening: A new discomfort glare indicator, Build. Environ. 88 (2015), 142–150.