comf (version 0.1.7)

calcpmvadj: Calculating Adjusted Predicted Mean Votes

Description

calcpmvadj calculates predicted mean votes (pmv) adjusted for cooling effect of elevated air speed, through the adaptive coefficient, or the expectancy factor.

Usage

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

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

epCoeff

expectancy factor e

Value

calcpmvadj returns the predicted mean vote adjusted for the cooling effect of elevated air speed.

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

calcePMV returns the predicted mean vote adjusted through the expectancy factor.

Details

apCoeff can be derived using calcapCoeff.

epCoeff can be derived using calcepCoeff.

calcePMV requires the actual sensation vote related to the physical data as it is required to alter the metabolic rate.

References

pmvadj is based on ASHRAE standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, Usa, 2013

apmv is based on Yao, R., Li, B. and Liu, J. A theoretical adaptive model of thermal comfort - Adaptive Predicted mean Vote (aPMV) Building and Environment, 2009, 44, 2089-209

epmv is based on Fanger, P. and Toftum, J. Extension of the PMV model to non-air-conditioned buildings in warm climates Energy and Buildings, 2002, 34, 533-536

See Also

see also calcComfInd, calcapCoeff, calcepCoeff

Examples

# NOT RUN {
## 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

# }