| Moderator:Jouni (see all) |
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What is the exposure in the Training assessment?
| Obs | Year | Unit | Result | Description |
|---|---|---|---|---|
| 1 | 2012 | ug/m3 | 10 | |
| 2 | 2020 | ug/m3 | 20 |
out <- formula.Op_en5674(dependencies.Op_en5674)
cat("Computed training exposures.\n")
print(out)
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formula.Op_en5674 <- function(x) {
data <- tidy(op_baseGetData("opasnet_base", x$curves[1]), direction = "wide")
curves <- strsplit(as.character(data$Parameters), split = ",")
coefficients <- data.frame(array(NA, dim = c(length(curves), 10)))
for(j in 1:length(curves)) {
coefficients[j, 1:length(curves[[j]])] <- curves[[j]]
}
coefficients[nrow(coefficients) + 1, 1] <- x$deviation
for(k in 1:ncol(coefficients)) {
coefficients[, k] <- ifelse(is.na(coefficients[, k]), 0, as.numeric(coefficients[, k]))
}
x.new <- polyroot(as.vector(colSums(coefficients)))
x.new <- ifelse(abs(Im(x.new)) < 1e-6, Re(x.new), 1e+30) # Take only the real roots.
x.new <- x.new[abs(x.new) == min(abs(x.new))] # Pick the root that is closest to zero (=BAU).
y.new <- 0
for(k in 1:ncol(coefficients)) {
y.new <- y.new + coefficients[, k] * x.new^(k-1)
}
temp <- data[1, ]
temp[1, ] <- "Deviation"
data <- rbind(data, temp)
data <- cbind(data, data.frame(Result = y.new))
return(data)
}
dependencies.Op_en5674 <- list(
curves = c("Op_en5478", "Cost curves for energy"),
bau = c("Op_en5469", "Energy balance in Kuopio"),
deviation = -5
)
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