Projects / Programmes source: ARIS

Calculation of Yearly Circadian Potential in Buildings Using Deep Learning Techniques (YCPdeep)

Research activity

Code Science Field Subfield
2.01.00  Engineering sciences and technologies  Civil engineering   

Code Science Field
2.01  Engineering and Technology  Civil engineering 
Daylight; Nonvisual effects of daylight; Luminous indoor environment; Machine learning; Circadian rhythms
Evaluation (rules)
source: COBISS
Data for the last 5 years (citations for the last 10 years) on May 18, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  138  2,140  1,819  13.18 
Scopus  181  2,687  2,307  12.75 
Researchers (7)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  39878  David Božiček  Civil engineering  Technical associate  2021  34 
2.  25423  PhD Tomaž Hozjan  Civil engineering  Researcher  2021 - 2024  302 
3.  25479  PhD Mitja Košir  Civil engineering  Head  2021 - 2024  480 
4.  34368  PhD Robert Pečenko  Mechanics  Researcher  2021  73 
5.  50604  PhD Jaka Potočnik  Civil engineering  Researcher  2021 - 2024  33 
6.  35705  PhD Nataša Šprah  Architecture and Design  Researcher  2022 - 2024  73 
7.  08437  PhD Goran Turk  Civil engineering  Researcher  2021 - 2024  524 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0792  University of Ljubljana, Faculty of Civil and Geodetic Engineering  Ljubljana  1626981  25,747 
The discovery of a new type of photoreceptors named intrinsically photosensitive Retinal Ganglion Cells (ipRGC) and related molecular mechanisms confirms the long-held suspicion that daylight is the most important synchronizer of daily circadian rhythm in humans. IpRGCs affect the secretion of melatonin – 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’s point of view, are limited to the point-in-time spectral content evaluation. Additionally, such simulations require high processing power and complex simulation setups. The main goal of the proposed project is to create a tool for predicting the health 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 35o to 60o northern geographic latitude. The tool will function on the basis of an artificial neural networks prediction 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 artificial neural networks model of the tool will be trained using simulation results, which will be obtained with the multispectral simulation tool ALFA. For the creation of the training database, a total of 13 input parameters will be modified: i) geometric parameters (i.e. width, depth and height of the room, window-to-wall-ratio); ii) optical parameters (i.e. reflectivity of walls, ceiling, and walls, the transmissivity of glazing) and iii) climate-related parameters (i.e. sky condition, latitude, time). 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 (i.e. Circadian Stimulus metrics - CS and alpha-optic lux metrics). 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 an artificial neural networks 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 scientific treatment daylighting and will enable the user to easily and quickly obtain the mentioned results on the basis of geometric, optical, and climatic input data. In the end, the functionality of the tool will directly contribute to better awareness of the professional and general public about the health impacts of daylighting in indoor environments. Therefore, promoting the UN’s 3rd Sustainable Development Goal – care for healthy living and the promotion of general well-being at all stages of life.
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