# about the dynamic glare classification categories

Hi all, I'm on the phase of analysing the DGP results obtained with evalglare from annual sample data.

I have two very simple questions about the glare classification method proposed by Wienold:

When evaluating the DGP occurence during the 95% of the working time, is not that clear for me if it should be considered the 'average' or the only the 'maximum' value of that sample.

Then, how to sample the data, should be randomly?

I mean, from a year of 2000 working hours, I should consider a random sample of 100 hours to estimate average DGP during the 5% of the time? Or, can that be selective? Since for instance winter time is more prone to achieve higher glare results.

I'd appreciate your help

Steph

Could you please elaborate on your process here? Which "glare classification method proposed by Wienold" are you referring to? Are you reviewing evalglare output from a point or a series of points?

Sorry about my late reply. In the paper: dynamic daylight glare evaluation (Building simulation conference, 2009), Wienold proposed a method to classify glare results for a certain period of time. They are classified taking into account the frequency of glare occurrence in 95% of the time and an average of the DGP (I'm comparing DGP) in the remaining 5% of the time. Three classes are considered, class 'A' for Best (below or equal 0.35 DGP), 'B' for Good (below/equal 0.40 DGP) and 'C' for Reasonable (below/equal 0.45 DGP).

First, I did my calculations by extracting (from the 100% of the data) the DGP values belonging to each classification (=< 0.35, =<0.40, =<0.45) to bin them, then determine the frequency occurence (% of total working hours) corresponding to each class. However, in the paper (table 6) they did this differently, by considering the maximum DGP value in 95% of the office time (yes, here I realised that is the maximum value, not the average); and the average in the 5% of the office time.

I wasn't sure how to determine which '95%' of the '100%' of the data to take into account, the maximum DGP value in summer would be lower than the maximum in winter (due to the sun position). At the end I think that this is just a matter of perception, the way how the data is classified and presented. I hope that I explained myself well, I was just curious about this. P.S. I'm analysing a serie of images (from one view point) but for a certain period of time (30 images, to account for 3 days, as a sample).