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solaR2 (version 0.11)

C_corrFdKt: Correlations between the fraction of diffuse irradiation and the clearness index.

Description

A set of correlations between the fraction of diffuse irradiation and the clearness index used by fCompD and fCompI.

Usage

## Monthly means of daily values
Ktm(sol, G0dm)
FdKtPage(sol, G0dm)
FdKtLJ(sol, G0dm)

## Daily values Ktd(sol, G0d) FdKtCPR(sol, G0d) FdKtEKDd(sol, G0d) FdKtCLIMEDd(sol, G0d)

## Intradaily values Kti(sol, G0i) FdKtEKDh(sol, G0i) FdKtCLIMEDh(sol, G0i) FdKtBRL(sol, G0i)

Value

A data.table, with two columns:

Fd

A numeric, the diffuse fraction.

Kt

A numeric, the clearness index(provided by the Kt functions).

Arguments

sol

A Sol object, it may be the result of the calcSol function.

G0dm

A Meteo object with monthly means of radiation. It may be the result of the readG0dm function.

G0d

A Meteo object with daily values of radiation. It may be the result of the readBDd (or equivalent) function.

G0i

A Meteo object with intraidaily values of radiation. It may be the result of the readBDi (or equivalent) function.

Author

Oscar Perpiñán Lamigueiro, Francisco Delgado López; The BRL model was suggested by Kevin Ummel.

References

  • Page, J. K., The calculation of monthly mean solar radiation for horizontal and inclined surfaces from sunshine records for latitudes 40N-40S. En U.N. Conference on New Sources of Energy, vol. 4, págs. 378–390, 1961.

  • Collares-Pereira, M. y Rabl, A., The average distribution of solar radiation: correlations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22:155–164, 1979.

  • Erbs, D.G, Klein, S.A. and Duffie, J.A., Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation. Solar Energy, 28:293:302, 1982.

  • De Miguel, A. et al., Diffuse solar irradiation model evaluation in the north mediterranean belt area, Solar Energy, 70:143-153, 2001.

  • Ridley, B., Boland, J. and Lauret, P., Modelling of diffuse solar fraction with multiple predictors, Renewable Energy, 35:478-482, 2010.

See Also

fCompD, fCompI

Examples

Run this code
library("data.table")
setDTthreads(2)

lat <- 37.2
BTd <- fBTd(mode = 'prom')
G0dm <- c(2.766, 3.491, 4.494, 5.912, 6.989, 7.742, 7.919, 7.027, 5.369,
    3.562, 2.814, 2.179)*1000;
Ta <- c(10, 14.1, 15.6, 17.2, 19.3, 21.2, 28.4, 29.9, 24.3, 18.2, 17.2,
    15.2)

prom <- readG0dm(G0dm = G0dm, Ta = Ta, lat = lat)
sol <- calcSol(lat = lat, BTd = BTd)

Kt <- Ktm(sol = sol, G0dm = prom)
Kt

Page <- FdKtPage(sol = sol, G0dm = prom)
LJ <- FdKtLJ(sol = sol, G0dm = prom)
Monthly <- merge(Page, LJ, by = 'Kt',
                suffixes = c('.Page', '.LJ'))
Monthly

xyplot(Fd.Page+Fd.LJ~Kt, data = Monthly,
       type = c('l', 'g'), auto.key = list(space = 'right'))

Kt = Ktd(sol = sol, G0d = prom)
Kt

CPR <- FdKtCPR(sol = sol, G0d = prom)
CLIMEDd <- FdKtCLIMEDd(sol = sol, G0d = prom)
Daily <- merge(CPR, CLIMEDd, by = 'Kt',
              suffixes = c('.CPR', '.CLIMEDd'))
Daily

xyplot(Fd.CPR + Fd.CLIMEDd ~ Kt, data = Daily,
       type = c('l', 'g'), auto.key = list(space = 'right'))

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