A new statistical methodology for classification of sky luminance distributions based on scan data

Markou, M.T.; Bartzokas, A.; Kambezidis, H. D.

doi:10.1016/j.atmosres.2007.06.001

Cited by 22 publications

(10 citation statements)

References 27 publications

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“…Acquired measurements are usually evaluated in accordance with 15 ISO/CIE sky standards described by the relative gradation and scattering indicatrix functions to categorize the sky types in a different way (Markou et al, 2007;Smola et al, 2012;Kobav et al, 2013;Kó mar et al, 2013). Moreover, creation of more-or-less precise sky luminance maps via standardly used interpolation techniques (minimum surface curvature, inverse distance to a power, modified Shepard's method, Kriging method) was studied and presented in .…”

Section: Introductionmentioning

confidence: 99%

Data regression on sphere for luminance map creation from sky scanner measurements

Petržala

Kómar

2015

Solar Energy

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Section: Introductionmentioning

confidence: 99%

Data regression on sphere for luminance map creation from sky scanner measurements

Petržala

Kómar

2015

Solar Energy

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“…That method has no application limits, and needs knowledge of the measured luminances. This method compares the measured values with the 15 types of CIE standard sky, respectively, and then selects the sky with the minimum RMSE value [12,15,28]. In this paper, the CIE sky model was not used for the analysis of indoor illuminance.…”

Section: Cie Standard General Sky Modelmentioning

confidence: 99%

“…Many researchers have evaluated a number of sky luminance distribution models [9][10][11][12][13]. Torres et al [14] studied the daylight conditions in Pamplona.…”

Section: Introductionmentioning

confidence: 99%

Sky Luminance Measurements Using CCD Camera and Comparisons with Calculation Models for Predicting Indoor Illuminance

Yun

Kim

2018

Sustainability

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Abstract:In this study, we address the algorithm of the calculation formula from sky luminance distribution to vertical illuminance and indoor illuminance. To predict daylight, studies about sky luminance distributions and the DeLight daylight model were investigated. This paper compared three well-known sky distribution models (Perez model, Igawa model, and CIE standard sky model) with measured data. The charge coupled device (CCD) camera was used as a measurement method for sky luminance distribution. Indoor illuminance values calculated with those well-known sky distribution models are compared with measured indoor illuminance data. The following conclusions were obtained: (1) In the results of R 2 , mean bias error (MBE), and Cv(RMSE) analysis, the CIE standard sky model showed the lowest error rate with measured data. Between the Perez model and Igawa model, the Igawa model showed a lower error rate; (2) When we compared the sky classification of the Perez model and the Igawa model, both models classified the sky similarly to the CIE standard sky model in March. However, the classification of the sky in the CIE standard sky model, the Perez model, and the Igawa model differed in some of the July data because of high solar elevation; (3) The illuminance of the center point of the room was calculated using the well-known sky luminance distribution model, the Igawa model has a lower error rate than the Perez model in Korea.

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“…Clear sky conditions can also be defined using instrumentation other than irradiance measurements. 95 Markou classified the sky condition based on sky luminance scan data (Markou et al, 2007). Dupont,96 Haeffelin, and Long compared the results of cloud detection using shortwave and longwave radiation and 97…”

mentioning

confidence: 99%

Identification of periods of clear sky irradiance in time series of GHI measurements

2016

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We present a simple algorithm for identifying periods of time with broadband GHI similar to that occurring during clear sky conditions from a time series of global horizontal irradiance (GHI) measurements. Other available methods to identify these periods do so by identifying periods with clear sky conditions using additional measurements, such as direct or diffuse irradiance. Our algorithm compares characteristics of the time series of measured GHI with the output of a clear sky model without requiring additional measurements. We validate our algorithm using data from several locations by comparing our results with those obtained from a clear sky detection algorithm, and with satellite and ground-based sky imagery.

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A new statistical methodology for classification of sky luminance distributions based on scan data

Cited by 22 publications

References 27 publications

Data regression on sphere for luminance map creation from sky scanner measurements

Data regression on sphere for luminance map creation from sky scanner measurements

Sky Luminance Measurements Using CCD Camera and Comparisons with Calculation Models for Predicting Indoor Illuminance

Identification of periods of clear sky irradiance in time series of GHI measurements

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