{"id":2414,"date":"2021-11-08T14:56:05","date_gmt":"2021-11-08T14:56:05","guid":{"rendered":"https:\/\/kske.fgg.uni-lj.si\/en\/?page_id=2414"},"modified":"2025-04-20T22:44:12","modified_gmt":"2025-04-20T22:44:12","slug":"project-ycpdeep","status":"publish","type":"page","link":"https:\/\/kske.fgg.uni-lj.si\/en\/project-ycpdeep\/","title":{"rendered":"PROJECT YCPdeep"},"content":{"rendered":"\n

PROJECT YCPdeep<\/strong> <\/p>\n\n\n\n

Calculation of Yearly Circadian Potential in Buildings Using Deep Learning Techniques<\/em><\/p>\n\n\n\n

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PROJECT DESCRIPTION:<\/strong><\/p>\n\n\n\n

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Recent discoveries in the field suggest that daylight is the most important daily circadian rhythm synchroniser in humans. The third photoreceptor type ipRGCs (intrinsically photosensitive Retinal Ganglion Cells) affect the secretion of melatonin \u2013 a sleep hormone that regulates the wake-sleep cycle. The response of the circadian phototransduction system is very different from the visual one, as it is maximally sensitive to light in the blue part of the spectrum and is time-conditioned. Light is desirable in the morning, but in the evening, it is recommended to avoid high light levels rich in blue light. Due to the complexity of estimating the circadian light content, this cannot be achieved with conventional visual daylighting simulation tools. Currently, tools capable of calculating the received spectral composition of radiation, which allow the evaluation of light from the circadian system\u2019s point of view, are limited to the point-in-time spectral content evaluation.<\/p>\n<\/div>\n\n\n\n

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The main goal of the YCPdeep project is to create a tool for predicting the \u201chealth\u201d potential of the indoor lighting environment that will reliably predict the circadian and visual part of the indoor daylighting environment in buildings from the user’s point of view. This will be enabled for a selected point-in-time, day, month, or whole year for locations between 35\u00ba to 60\u00ba northern geographic latitude. The tool will function on the basis of an neural networks model and will be able to reliably predict circadian light using basic data about geometric and optical properties of the considered space as well as climatic data of the location. The proposed variation of the parameters will ensure the general applicability of the devised tool. The results of each of the iterations of multispectral simulations will be assessed using state-of-the-art methods for evaluating the circadian light content. The artificial neural networks model, which will be created on the basis of the mentioned simulation database, will be able to predict visual and circadian daylighting for any selected time of a year. Such a model will then be implemented in an online tool for evaluation of a healthy daylighting environment, which in addition to light quantities at a certain point-in-time will also allow estimation of the average amounts of circadian and visual light, while evaluating the duration of exposure to circadian and\/or visually appropriate daylight for a period of a day, month or whole year using the newly proposed metrics of climate-based circadian light. The proposed tool will be the first of its kind in the field of daylighting and will enable the user to easily and quickly obtain the mentioned results on the basis of geometric, optical, and climatic input data.<\/p>\n\n\n\n

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PROJECT SCHEDULE:<\/strong><\/p>\n\n\n\n

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RESEARCH TEAM:<\/strong><\/p>\n\n\n\n