A deep convolutional neural network based on U-Net to predict annual luminance maps

References

• Arnold, Barry C. 2014. “Pareto Distribution.” Wiley StatsRef: Statistics Reference Online.  doi:https://doi.org/10.1002/9781118445112.stat01100.pub2.
   Google Scholar
• Dave, Vachik S., and Kamlesh Dutta. 2014. “Neural Network Based Models for Software Effort Estimation: a Review.” Artificial Intelligence Review 42 (2): 295–307.
   Web of Science ®Google Scholar
• Goodfellow, Ian. 2016. Deep Learning. Vol. 1. No. 2. Cambridge: MIT press.
   Google Scholar
• He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. “Deep Residual Learning for Image Recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition.
   Google Scholar
• Jakubiec, J. Alstan, and Christoph F. Reinhart. 2011. “DIVA 2.0: Integrating Daylight and Thermal Simulations Using Rhinoceros 3D, Daysim and EnergyPlus.” Proceedings of Building Simulation 20 (11): 2202–2209.
   Google Scholar
• Jones, Nathaniel L., and Christoph F. Reinhart. 2014. “Irradiance Caching for Global Illumination Calculation on Graphics Hardware.” Proceedings of the ASHRAE/IBPSA-USA Building Simulation conference.
   Google Scholar
• Jones, Nathaniel L., and Christoph F. Reinhart. 2017. “Experimental Validation of ray Tracing as a Means of Image-Based Visual Discomfort Prediction.” Building and Environment 113: 131–150.
   Web of Science ®Google Scholar
• Kingma, Diederik P., and Jimmy Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv Preprint ArXiv 1412.6980.
   Google Scholar
• Laouadi, Abdelaziz, C. F. Reinhart, and D. Bourgeois. 2008. “Efficient Calculation of Daylight Coefficients for Rooms with Dissimilar Complex Fenestration Systems.” Journal of Building Performance Simulation 1 (1): 3–15.
   Google Scholar
• Lee, Eleanor S., David Geisler-Moroder, and Gregory Ward. 2018. “Modeling the Direct sun Component in Buildings Using Matrix Algebraic Approaches: Methods and Validation.” Solar Energy 160: 380–395.
   Web of Science ®Google Scholar
• Lin, Min, Qiang Chen, and Shuicheng Yan. 2013. “Network in network.” arXiv preprint arXiv:1312.4400 (2013).
   Google Scholar
• Liu, Yue, Alex Colburn, and Mehlika Inanici. 2018. “Computing Long-Term Daylighting Simulations from High Dynamic Range Imagery Using Deep Neural Networks.” Building Performance Analysis Conference and SimBuild Co-Organized by ASHRAE and IBPSA-USA, Chicago, IL, September 26–28, 119–126.
   Google Scholar
• Liu, Yue, Alex Colburn, and Mehlika Inanici. 2019. “Predicting Annual Equirectangular Panoramic Luminance Maps Using Deep Neural Networks.” Proc. Of International conference on Building Simulation BS2019, Rome (Italy).
   Google Scholar
• Liu, Yue, Alex Colburn, and Mehlika Inanici. 2020. “Deep Neural Network Approach for Annual Luminance Simulations.” Journal of Building Performance Simulation 13 (5): 532–554.
   Web of Science ®Google Scholar
• LM, IES. 2013. “Approved method: IES spatial Daylight autonomy (sDA) and annual sunlight exposure (ASE).” Illuminating Engineering Society. https://www.ies.org/product/ies-spatial-daylight-autonomy-sda-and-annual-sunlight-exposure-ase.
   Google Scholar
• McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” The Bulletin of Mathematical Biophysics 5 (4): 115–133.
   Google Scholar
• Ochoa, Carlos E., Myriam BC Aries, and Jan LM Hensen. 2012. “State of the art in Lighting Simulation for Building Science: a Literature Review.” Journal of Building Performance Simulation 5 (4): 209–233.
   Web of Science ®Google Scholar
• Pan, Sinno Jialin, and Qiang Yang. 2009. “A Survey on Transfer Learning.” IEEE Transactions on Knowledge and Data Engineering 22 (10): 1345–1359.
   Web of Science ®Google Scholar
• Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “Pytorch: An Imperative Style, High-Performance Deep Learning Library.” Advances in Neural Information Processing Systems 32: 8026–8037.
   Google Scholar
• Perez, Richard, Robert Seals, and Joseph Michalsky. 1993. “All-weather Model for sky Luminance Distribution—Preliminary Configuration and Validation.” Solar Energy 50 (3): 235–245.
   Web of Science ®Google Scholar
• Ren, Peiran, Yue Dong, Stephen Lin, Xin Tong, and Baining Guo. 2015. “Image Based Relighting Using Neural Networks.” ACM Transactions on Graphics (ToG) 34 (4): 1–12.
   Web of Science ®Google Scholar
• Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. 2015. “Faster r-cnn: Towards real-time object detection with region proposal networks.” arXiv preprint arXiv:1506.01497.
   Google Scholar
• Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-net: Convolutional Networks for Biomedical Image Segmentation.” International Conference on medical image computing and computer-assisted intervention. Springer, Cham
   Google Scholar
• Roudsari, Mostapha Sadeghipour, Michelle Pak, and Adrian Smith. 2013. “Ladybug: A Parametric Environmental Plugin for Grasshopper to Help Designers Create an Environmentally-Conscious Design.” Proceedings of the 13th international IBPSA conference held in lyon, France, August. http://www.ibpsa.org/proceedings/bs2013/p_2499.pdf.
   Google Scholar
• Saxena, Mudit, G. Ward, T. Perry, and L. Heschong. 2010. “Dynamic Radiance–Predicting Annual Daylighting with Variable Fenestration Optics Using BSDFs.” Proceedings of SimBuild 4 (1): 402–409.
   Google Scholar
• Torrey, Lisa, and Jude Shavlik. 2010. “Transfer Learning.” Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by E. S. Olivas, J. D. M. Guerrero, M. Martinez-Sober, J. R. Magdalena-Benedito, and A. J. S. López, 242–264. Hershey, PA: IGI Global.
   Google Scholar
• Tregenza, Peter R., and I. M. Waters. 1983. “Daylight Coefficients.” Lighting Research & Technology 15 (2): 65–71.
   Google Scholar
• Van Den Wymelenberg, Kevin, and Mehlika Inanici. 2016. “Evaluating a new Suite of Luminance-Based Design Metrics for Predicting Human Visual Comfort in Offices with Daylight.” Leukos 12 (3): 113–138.
   Web of Science ®Google Scholar
• Wang, Zhou., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612.
   PubMed Web of Science ®Google Scholar
• Ward, Gregory J. 1994. “The RADIANCE Lighting Simulation and Rendering System.” Proceedings of the 21st annual conference on computer graphics and interactive techniques.
   Google Scholar
• Ward, G., R. Mistrick, E. S. Lee, A. McNeil, and J. Jonsson. 2011. “Simulating the Daylight Performance of Complex Fenestration Systems Using Bidirectional Scattering Distribution Functions Within Radiance.” Leukos 7 (4): 241–261.
   Web of Science ®Google Scholar
• Wienold, Jan, and Jens Christoffersen. 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.
   Web of Science ®Google Scholar