2020
DOI: 10.1080/19401493.2019.1711456
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A dimensionality reduction method to select the most representative daylight illuminance distributions

Abstract: One challenge when evaluating daylight distribution is dealing with the large amount of temporal and spatial data, visualisations and variability in illuminances that are assessed in buildings. Using a dimensionality reduction method based on principal component analysis, we identified the most representative annual daylight distributions. We modelled a rectangular room containing an analysis grid of 3200 illuminance sensor points and simulated 3285 different temporal daylight conditions using an annual occupa… Show more

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Cited by 14 publications
(10 citation statements)
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“…Any departure from perpendicularity reduces the effective collection area, leading to decreased light intake and potential losses, as light may escape the system. This consideration becomes particularly important when factoring in the varying position of the light source [29][30][31][32][33][34][35][36]. These findings indicate a common decrease in illuminance at the upper and lower termini of both types of tubes in response to an increase in the incident elevation angle of the light source.…”
Section: Resultsmentioning
confidence: 96%
See 1 more Smart Citation
“…Any departure from perpendicularity reduces the effective collection area, leading to decreased light intake and potential losses, as light may escape the system. This consideration becomes particularly important when factoring in the varying position of the light source [29][30][31][32][33][34][35][36]. These findings indicate a common decrease in illuminance at the upper and lower termini of both types of tubes in response to an increase in the incident elevation angle of the light source.…”
Section: Resultsmentioning
confidence: 96%
“…At each position, the internal illuminance values gradually decreased as the angle of the incoming light increased from 0 • to 90 • . The uniformity of illuminance distribution at the bottom end positions of the aluminum alloy hollow light pipe was assessed using two key ratios: "min to max" and "min to average" illuminances [31]. The "min to max" ratio, which is close to 1, signifies minimal variation in lighting levels, with the lowest illuminance nearly equal to the highest illuminance, ensuring uniform lighting distribution.…”
Section: Resultsmentioning
confidence: 99%
“…Principal component analysis was performed for each index, with the soil and root traits as explanatory variables (Figure 8). The x-and y-axes represent the unit-less eigenvectors, elucidating the magnitude of variance explained in a certain direction by the principal components, irrespective of their positive or negative values [45,46]. The first two principal components explained 68.0% of the variation in the data.…”
Section: Analysis Of Soil Water Distributionmentioning
confidence: 99%
“…Lee et al [17] showed that configurations of single light source directions exist that are effective for face recognition. Kent et al [18] used principal component analysis to find the most representative spatial daylight distribution patterns. Savelonas et al [19] provided an overview of computational methods aiding geoscientists in the analysis of 2D or 3D imaging data.…”
Section: Facial Pose Transfermentioning
confidence: 99%