Assessing daylight performance in use: A comparison between long-term daylight measurements and simulations

Brembilla, Eleonora; Drosou, Nafsika; Mardaljevic, John

doi:10.1016/j.enbuild.2022.111989

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Unlike the absolute analysis, the climate-based daylight model (CBDM) utilises various sky conditions and models based on solar irradiance and illuminance data in weather files to analyse seasonal and annual building daylight performance [7]. CBDM dynamic and long-term analysis made it suitable for assessing building daylighting impact on other crucial aspects of the building, such as energy use intensity and thermal behaviour.…”

Section: Climate-based Daylight Model Analysismentioning

confidence: 99%

“…Thus, sDA is defined as the percentage of floor area that exceeds a minimum daylight illuminance for a specified percentage of hours in a year [39,40]. As proposed by the IESNA [7,39], a 300 lux minimum daylight illuminance (the light level threshold for general office tasks in South Africa) and 50% of hours from 07h00 to 17h00 daily (including weekends and holidays) in a year (sDA 300,50% ) were adopted in the research. Lastly, UDI is the percentage of occupied time (07h00 to 17h00) in a year that a point in a given space is within a specified illuminance class, usually 100 to 2000 lux [31,41,42].…”

Section: Climate-based Daylight Model Analysismentioning

confidence: 99%

“…However, the choice of UDI upper limit has varied among researchers over the years, with Li et al [6] alluding that daylighting above 2000 lux may result in overheating and glare. Brembilla et al [7] used 3000 lux as the UDI upper limit but obtained the fewest instances with vertical illuminance in this range. On the other hand, Hong et al [8] found 70.92% and 71.88% UDI 300-3000 for a single-glazed window and doubled-glazed window, respectively, in a typical office room in China.…”

Section: Introductionmentioning

confidence: 99%

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Daylighting Assessment of a Heritage Place of Instruction and Office Building in Alice, South Africa

Overen,

Meyer,

Makaka

2023

Buildings

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Mitigation of post-occupancy building energy consumption has resulted in the change of building design to utilise ambient weather factors for indoor thermal conditioning and lighting. This has increased the construction of new buildings with large glazing façades and reduced adaptive use of heritage buildings as they are considered not designed to meet modern energy use requirements. This paper evaluates the daylighting performance of a heritage place of instruction and office building. A quantitative research approach based on building information simulation tools was adopted. Autodesk Revit 2021 and Integrated Environmental Solutions Virtual Environment (IESVE) 2021 were used in modelling and simulating the building daylighting performances. The building’s annual daylight performance analysed with climate-based daylight modelling shows that points in the analysed spaces were within the UDI300-2000 for more than 50% of the occupied period (07h00 to 17h00) in a year. The sDA300,50% was found to be 100% in most spaces, which is considered a favourable daylit space according to the Illuminance Engineering Society of North America (IESNA). Further, discomfort glare analysis revealed that the building daylight glare is imperceptible, with an average daylight glare probability of 21.2%. The 1:14 window–wall ratio contributes to the building daylighting relative to orientation without constituting visual discomfort. Overall, climate-based daylight modelling revealed that the building’s annual daylight level meets the IESNA requirements with an imperceptible daylight glare.

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Section: Climate-based Daylight Model Analysismentioning

confidence: 99%

Section: Climate-based Daylight Model Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Daylighting Assessment of a Heritage Place of Instruction and Office Building in Alice, South Africa

Overen,

Meyer,

Makaka

2023

Buildings

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Ensemble machine learning framework for daylight modelling of various building layouts

Alsharif,

Arashpour,

Golafshani

et al. 2023

Build. Simul.

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The application of machine learning (ML) modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment. Although many advancements have been made, no standardized ML modelling framework exists in daylight assessment. In this study, 625 different building layouts were generated to model useful daylight illuminance (UDI). Two state-of-the-art ML algorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were employed to analyze UDI in four categories: UDI-f (fell short), UDI-s (supplementary), UDI-a (autonomous), and UDI-e (exceeded). A feature (internal finish) was introduced to the framework to better reflect real-world representation. The results show that XGBoost models predict UDI with a maximum accuracy of R2 = 0.992. Compared to RF, the XGBoost ML models can significantly reduce prediction errors. Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.

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A review on the current usage of machine learning tools for daylighting design and control

Ngarambe

Adilkhanova

Uwiragiye

et al. 2022

Building and Environment

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Assessing daylight performance in use: A comparison between long-term daylight measurements and simulations

Cited by 10 publications

References 17 publications

Daylighting Assessment of a Heritage Place of Instruction and Office Building in Alice, South Africa

Daylighting Assessment of a Heritage Place of Instruction and Office Building in Alice, South Africa

Ensemble machine learning framework for daylight modelling of various building layouts

A review on the current usage of machine learning tools for daylighting design and control

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