Daylight measurement error

Hayman, Simon

doi:10.1191/1477153503li086oa

Cited by 8 publications

(6 citation statements)

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“…Moreover, they are a highly effective method for predicting the qualitative and quantitative effects of the daylighting design [15]. Thanachareonkit et al [16], Hayman [17] and Cannon-Brookes [18] investigated the accuracy of physical models in measuring daylighting and showed significant errors causing large discrepancies of 30% and more, in terms of relative divergences [16]. In order to avoid sources of error and achieve reliable results, the current study set out two main strategies.…”

Section: Experimental Setup For Physical Model Testingmentioning

confidence: 98%

Interactions between louvers and ceiling geometry for maximum daylighting performance

2009

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a b s t r a c tThe impact of ceiling geometries on the performance of louvers was investigated using physical model experiments and Radiance simulations. Two performance indicators, the illuminance level and its distribution uniformity, were used to assess daylighting performance in a room located in a subtropical climate region. It was found that the performance of the louvers can be improved by changing the ceiling geometry. The illuminance level increased in the rear of the room, and decreased in the frontdnear the windowdcompared to rooms having horizontal ceilings. Radiance results were found to be in good agreement with physical model data obtained under a clear sky and high solar radiation. Louvers' daylighting performance was reduced by tilting the louvers downward. The best ceiling shape was found to be one that is chamfered in the front and rear of the room.

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Section: Experimental Setup For Physical Model Testingmentioning

confidence: 98%

Interactions between louvers and ceiling geometry for maximum daylighting performance

2009

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“…Errors exist in physical measurements of artificial lighting (Moore 1980) and daylighting (Hayman 2003). Concerning lighting simulation, CIE estimated (Fisher 1992) that an acceptable range would be 10% for average illuminance calculations, 20% for measured point values, but very difficult to reach 5% in complex situations.…”

Section: D) Accuracy Levelsmentioning

confidence: 99%

“…However, measured data for validation might not be available to all researchers or model users (Mardaljevic 2004). Care must be taken with data accuracy, in particular under sunny conditions (Hayman 2003).…”

Section: Abstraction Of Reality For Inputmentioning

confidence: 99%

State of the art in lighting simulation for building science: a literature review

Ochoa

Aries

Hensen

2012

Journal of Building Performance Simulation

119

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, J. L. M. (2012). State of the art in lighting simulation for building science : a literature review. Journal of Building Performance Simulation, 5(4), 209-233. DOI: 10.1080209-233. DOI: 10. /19401493.2011 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. This paper examines the current state of the art in lighting simulation related to building science research. Discussion on historical developments and main modelling approaches is followed by describing lighting simulation within the design process, where it is applied beyond presentation renderings. Works are grouped using the main aspects of a program (input, modelling, and output). Lighting simulation currently focuses on representing accurately a large number of common situations encountered by building designers and researchers. Existing models apply roughly the same theoretical algorithms and calculation aids, limiting representation of certain physical phenomena. Although some models can be used for element design, they are not practical enough to develop or prototype new, untested elements. Elaborate building components require separate analysis through complex simulation aids. Few tools support the early architectural design process. Simplification applies when integrating lighting simulation to whole-building simulation. Input quality affects accuracy, while output needs careful expert interpretation.

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“…The illuminance measurements were recorded on table tops, at approximately 750 mm above floor level. This was because it provided a flat surface and this is the typical height in which daylight measurements are recorded (Hayman 2003). The relative error percentage was calculated for each spot measurement and the mean relative error percentage was calculated for the monitored space.…”

Section: Visual Field Comparisonmentioning

confidence: 99%

“…All lighting equipment's were set so that illuminance measurements were recorded every ten minutes for two hours. All the light meters are within ±10% of each other therefore they do not need to be sent back for further calibration (Hayman 2003).…”

Section: Appendixmentioning

confidence: 99%

HDR luminance measurement: Comparing real and simulated data

Au¹

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<p>The objective of this research was to determine the adequacy of Android devices capturing High Dynamic Range (HDR) photography, and using it as a tool for daylight analysis in New Zealand’s commercial building stock. This study was conducted with an Android Smartphone and later an Android Tablet, employing the use of a US$50 magnetic fisheye lens. The overall aim of this research was to evaluate whether an inexpensive programmable data acquisition system could provide meaningful and useful luminance data. To complete this research, the adequacy of computer simulation using HDR photography of the real horizontal and vertical skies was explored. Using the method documented in this research, the luminance distribution of the building interiors could then be mapped accurately in daylight simulations. The BRANZ Building Energy End-Use Study (BEES) team currently have one internal lighting measurement point, which records light levels in each of more than 100 commercial buildings randomly selected to be representative of commercial buildings in New Zealand. The HOBO U12 data logger typically records the environmental data on a desktop within the main area of the monitored premises. The HOBO data loggers only provide the environmental measurement of that specific location and do not provide the researcher the daylight distribution of the whole space. Using the data collected by BEES, a thesis was developed to explore the utility of HDR imaging as a supplement to the use of a single internal light measurement in the analysis of daylight potential in New Zealand’s commercial building stock. Three buildings were randomly selected from the BEES targeted strata five database to be monitored over a one day period. Within each building, at least three rooms were studied, all facing different orientations. The pilot study and the first two buildings monitored employed the use of a Motorola Defy Smartphone to capture the low dynamic range (LDR) photographs of each scene using both the HDR Camera application available from the Android Google Play Application Store, and the built-in camera application that came with the Smartphone. The vertical (by pressing the Smartphone hard up against the window) and horizontal (from the ground) skies were also captured simultaneously as only one device was available at each monitored building and to ensure consistency in each building. These photographs were fused using an HDR software called Photosphere, into a single HDR image. However, before the HDR images could be generated to contain accurate luminance data within the images, a camera response curve is required to be generated. A camera response curve is unique to each device and only needs to be generated once and can be generated using Photosphere. Unfortunately, a camera response curve could not be generated for the Motorola Defy Smartphone and through various experimentations and tests in both the lighting laboratory and in-field, it was discovered that this had nothing to do with the EXIF data contained within the photographs captured as originally thought, but the JPEG image format itself. This resulted in a generic camera response curve, from Photosphere, being used for the pilot study and the first two monitored buildings. For the final building that was monitored, a Galaxy Note Tablet was used. A camera response curve for this device could be easily generated using Photosphere. The pilot study and three monitored buildings were geometrically simulated using Google SketchUp 8 and were then exported in to Radiance Lighting Simulation and Rendering System using the su2rad plug-in. The files were then edited in Ecotect™ Radiance Control Panel, after which the real and simulated images were compared using HDRShop and RadDisplay. The four comparison methods were used to compare the real and simulated data were pixel to pixel comparison; section to section pixel comparison; surface to surface comparison and visual field comparison. Of the four methods used the first two were visual based comparisons, whereas the latter two were numerical, which employ the use of a calculation method to calculate the relative error percentages. The biggest problem that arose from the visual comparisons was the geometrical misalignment due to the use of a fisheye lens and only provided the luminance difference ranging from a scale of 0 cd/m2 to 50 cd/m2. The numerical comparison methods provided a 60% correlation between real and simulated data. It was concluded that, depending on the Android device used, HDR photographs are able to provide reliable images that contain accurate luminance data when a camera response curve for the device could be generated.</p>

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Daylight measurement error

Cited by 8 publications

References 5 publications

Interactions between louvers and ceiling geometry for maximum daylighting performance

Interactions between louvers and ceiling geometry for maximum daylighting performance

State of the art in lighting simulation for building science: a literature review

HDR luminance measurement: Comparing real and simulated data

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