comf (version 0.1.12)

calcaPMV: Adaptive Predicted Mean Votes

Description

Function to calculate adaptive Predicted Mean Vote (aPMV) adjusted through the adaptive coefficient.

Usage

calcaPMV(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, apCoeff)

Value

calcaPMV returns the predicted mean vote adjusted through the adaptive coefficients.

Arguments

ta

a numeric value presenting air temperature in [degree C]

tr

a numeric value presenting mean radiant temperature in [degree C]

vel

a numeric value presenting air velocity in [m/s]

rh

a numeric value presenting relative humidity [%]

clo

a numeric value presenting clothing insulation level in [clo]

met

a numeric value presenting metabolic rate in [met]

wme

a numeric value presenting external work in [met]

apCoeff

adaptive coefficient lambda

Author

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

Details

apCoeff can be derived using calcapCoeff.

References

aPMV is based on Yao, Li and Liu (2009) <doi:10.1016/j.buildenv.2009.02.014>

See Also

calcComfInd, calcapCoeff

Examples

## Note. Due to random generated asv values. The values for the coefficients will not be meaningful.
## Create sample data
ta  <- 20:24     # vector with air temperature values
tr  <- ta         # vector with radiant temperature values
vel <- rep(.1,5)  # vector with air velocities
rh  <- rep(50,5)  # vector with relative humidity values
clo <- rep(1.0,5) # vector with clo values
met <- rep(1.1,5) # vector with metabolic rates
asv <- rnorm(5)   # vector with actual sensation votes
lsCond <- as.list(data.frame(ta,tr,vel,rh,clo,met,asv))
## Calculate coefficient apCoeff for data set
apCoeff <- calcapCoeff(lsCond)
## calculate apmv
apmv <- NULL
for (i in 1:length(ta)){
 apmv[i] <- calcaPMV(ta[i], tr[i], vel[i], rh[i], clo[i], met[i], apCoeff = apCoeff)$apmv}
apmv