I made a survey of 777 participants about outdoor thermal comfort sensation along with micrometeorological onsite measurements. With the data I?ve calculated PET with RayMan for each participant and clustered those into PET bins of 1°C interval, and then assigned to each bin the corresponding average of the Thermal Sensation Votes (-3 to +3 thermal sensation scale) given by the participants, according to the corresponding PET bin, and obtaining the Mean Thermal Sensation Votes (MTSV) for each PET bin.
Then, in order to define the neutral temperature of men and women I fitted a liner regression line produced from plotting MTSVs against each PET bin, obtaining a regression equation for each gender?s MTSV: y=-2.4+0.08x (men) and y=-2.95+0.09x (women).
The problem is that by solving for zero in the equations the neutral temperature was determined to be 30 ?C (men) and 32.7 ?C (women)? That?s way too high!
This is Cuenca- Ecuador, a temperate climate, and according to literature it should be a neutral zone between 18 to 23 °C. I?ve checked out all variables over and over again and these are correctly introduced, the only thing that lowers PET results is by arbitrary lowering the Global radiation G (W/m2), but this data is introduced according to local measurements. I?ve employed input Datafile and Sky view factor only.
What could be the problem? Do you have any suggestions? Thank you.
hm, this is hard to tell. There's lots things, that can go wrong. Maybe you could provide us with some more details about your calculations and settings, so we can make a better guess??
DATE AND TIME & CURRENT DATA: tab-delimited text file introduced in Datafile, without headlines and containing data-columns in this order: enumerator, date, day of year, time, air-temperature, relative-humidity, wind-velocity, cloud-cover, global-radiation. Example: 1 23.1.2017 23 13:19 28.5 21.5 0.14 6 976.5 2 23.1.2017 23 16:12 26.1 33.4 1.5 6 612.5 3 23.1.2017 23 17:17 19.4 50.7 0.24 2 517.5
GEOGRAPHIC DATA: Location added as ?Ecuador (Cuenca)? Geogr. longitude (..°..? E) -79°0? Geogr. latitude (..°..? N) -2°53? Altitude (m) 2560 time zone (UTC + h) -5.0
PERSONAL DATA: The 777 questionnaires were introduced in two groups: Men: Height (m) 1.70, weight (kg) 69 Women: Height (m) 1.58, weight (kg) 58 Each group was divided into four age ranges: 15, 29, 53 and 70 years old. So in total eight groups were analyzed.
CLOTHING AND ACTIVITY: Each of the 8 groups were classified in 6 different categories according to the data obtained in the survey: Exercising: 0.64 (clo), 205 (W) Walking: 0.82 (clo), 115 (W) Standing: 0.78 (clo), 70 (W) Seated: 0.82 (clo), 60 (W) Lean back: 0.81 (clo), 45 (W) Sleeping: 0.86 (clo), 40 (W).
SKY VIEW FACTOR: This is the only additional input data. Two different fisheye photos were taken on site for the two different sites studied. The sky in the photos was previously selected in Photoshop and leaved all white.
All information was introduced according to the measurement data obtained from two Delta OHM HD 32.1 Thermal Microclimates and from the questionnaires. Local Airport and University weather stations provided the information for cloud cover and global radiation G(W/m2). By arbitrary changing the data, I found that the only variable which affects PET temperatures in a significant way is by lowering the global radiation, which according to the local stations ranges from 62.5 to 1099 G(W/m2), from 8am to 6pm.
I hope this could help us realize what could be wrong, otherwise neutral temperature actually may be the result obtained for the surveyed population...
date and day of year are redundant. Please decide for one of them and remove the other one.
Same holds for cloud-cover and global radiation. As your measured global radiation is (hopefully) more precise than some astronomic approximation, best go for that one and remove the clouds.
In the results, check the mean radiant temperature for plausibility.
As you used SVF, please also check for plausibility (make sure SVF is not 0.0).
If you are unsure, post one line that produces suspect results together with the table header here in the forum, so I can have a look at it.
Given that the change is so drastic, would it be methodologically correct if all calculations are performed just with the cloud cover data instead of global radiation?
to be able to judge what you did, I still need one line of each of your _input_ files for the same date and time (the one with global radiation, as well as the one with cloud cover). I still suspect the error is located either in the input files or in the settings.
i checked with my copy of RayMan an can confirm the strong disagreement in Tmrt between the two calculations based on cc (49.3°C) and G (64.4°C).
I suspect two things here. First, the parametrization for the estimation of G based on astronomic calculations might not be sufficiently precise for a height of 2560m. I can read this from the Gmax value that is calculated to be 819.9 W/m², that is less than your measured data. However and secondly, the cloud cover of 6/8 does not match the very high value of measured global radiation, you claim is 951.0 W/m². This might point to cloud side reflection, that can not be considered by RayMan.
For both reasons, I'd strongly suggest to prefer the global radiation measurements over the cloud cover for Tmrt calculations.
Thank you. Then, coming back to the first inquiry, what would be in your opinion a logical conclusion for this?:
"in order to define the neutral temperature of men and women I fitted a liner regression line produced from plotting MTSVs against each PET bin, obtaining a regression equation for each gender?s MTSV: y=-2.4+0.08x (men) and y=-2.95+0.09x (women).
The problem is that by solving for zero in the equations the neutral temperature was determined to be 30 ?C (men) and 32.7 ?C (women)? That?s way too high"
In this case, using all the gathered data in the same manner as the samples previously sent, would it be conclusive that according to calculations performed by RayMan, the neutral temperature for people in this geographic zone tends to be those values?
I?ve decided to use primary information in order to calculate the mean radiant temperature, given the fact that I?ve measured the following data on-site: globe temperature (°C), wind velocity (m/s) and air temperature (°C). Therefore I?ve employed the formula for Tmrt recommended at EN ISO 7726:2001, considering forced ventilation. PET temperature dropped as a result.
So now I?ve used directly my results of Tmrt in RayMan (datafile). The final inputs I?m using in datafile are six: Date, Time, Ta, RH, wind velocity and Tmrt. Besides I?m also using fisheye photos for the sky view factor, geographic data, personal data, clothing & activity.
According to literature the reason RayMan uses: date, time, sky view factor and geographic data is as inputs for Tmrt calculation.
Given that I already have Tmrt, Should I stop using one of these previous just mentioned? I?m almost sure that date, time, and geographic data are still required, if so; Should I stop using the fisheye photos (sky view factor) given that I already have Tmrt?
My intention is to get the most accurate or ?realistic? results possible, (even though I?m kind of running out of time for this research, so I really thank you if you could answer me the sooner it might be possible for you).
the datafile you are using is just fine. The additionally FishEye is not required, but should not do any harm (so you don't need to recalculate if you used one).
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07.04.2017 19:47
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