Difference between revisions of "Building model"

library(OpasnetUtils)

#########3# Calculate all building events (constructions, demolitions, renovations)

buildings <- Ovariable("buildings", 
	dependencies = data.frame(Name = c(
		"buildingStock", # Current building stock
		"heatingShares", # Heating types of current buildings
		"efficiencies", # Energy efficiencies of current buildings
		"construction", # Construction rate in the future
		"efficienciesNew", # Energy efficiencies in the future
		"buildingTypes", # A dummy variable to combine two different indices: Building and Building2
		"heatingSharesNew", # Heating types of the buildings in the future
		"renovationShares", # Fraction of renovation type when renovation is done.
		"renovation", # Percentage of renovations per year
		"eventyear", # A dummy variable to combine time periods to numerical time axis.
		"obsyear" # Years for which observations are calculated. This requires timepoints function.
	)),
	formula = function(...) {
		# Current building stock
		Now <- buildingStock * heatingShares * efficiencies * eventyear
		
		# Construction in ten years multiplied by fraction of energy classes and heating types.
		New <- construction * 10 * efficienciesNew * buildingTypes * heatingSharesNew * eventyear
		
		temp1 <- EvalOutput(Ovariable("temp1", data = orbind(Now, New))) # All construction
		temp1 <- unkeep(temp1, cols = "Building2", sources = TRUE, prevresults = TRUE)
		# All source indices can be unkept, because each has only one location.

		renovationyear <- Ovariable("renovationyear", data = data.frame(
			Renovationyear = c(2015, 2025, 2035, 2045),
			Age = NA,
			Result = 1
		))
		reno <- temp1 * renovationyear # reno is the building stock repeated for every potential renovation decade.
		
		reno@output$Age <- reno@output$Renovationyear - reno@output$Eventyear

		# Floor area of renovations in ten years. Combine with continuous index Age.
		reno <- continuousOps(reno, renovation, '*') * renovationShares * 10 
		reno <- unkeep(reno, cols = c("Age", "Renovationyear"), sources = TRUE, prevresults = TRUE)

		temp2 <- orbind(temp1 * 1, reno * -1) # Equal amount stops being non-renovated.
		temp2$Renovation <- "None"
		temp2 <- orbind(temp2, reno) # Temp2: Construction + renovation in data.frame
		temp2$Renovation <- as.factor(temp2$Renovation)

		temp3 <- EvalOutput(Ovariable("buildings", data = temp2)) # Temp3: Like Temp2 but ovariable
		temp3 <- unkeep(temp3, sources = TRUE, prevresults = TRUE)
		temp3@output <- fillna(temp3@output, marginals = colnames(temp3@output)[temp3@marginal])
		colnames(temp3@output)[colnames(temp3@output) == "City area"] <- "City.area"

		# temp4: Drop redundant time indices
		temp4 <- oapply(temp3, cols = c("Constructed", "Renovationyear", "Age"), fun = "sum", na.rm = TRUE)
		temp4@output <- temp4@output[!is.na(result(temp4)) , ]

		# Calculate cumulative events at timepoints defined by obsyear.
		out <- timepoints(temp4, obsyear = obsyear)
		return(out)
	}
)

### HeatingEnergy

heatingEnergy <- Ovariable("heatingEnergy", 
	dependencies = data.frame(Name = c(
		"energyUse", 
		"savingPotential", 
		"buildings",
		"buildingTypes"
	)),
	formula = function(...) {
		
		out <- buildings * buildingTypes * energyUse * savingPotential
		
		return(out)
	}
)

# Calculate the cumulative impact of the events on building stock to given years

timepoints <- function(X, obsyear) {
	# Function timepoints takes an event list and turns that into existing crosscutting situations at 
	# timepoints defined by years.
	# X must be an ovariable with index Eventyear.
	# obsyear must be a vector of years.

	if(is.factor(X@output$Eventyear))
		X@output$Eventyear <- as.numeric(levels(X@output$Eventyear)[X@output$Eventyear])
	# tapply (and therefore oapply) changes continuous indices to factors! Must change back by hand.

	out <- data.frame()

	for(i in obsyear) {
		out <- rbind(out, data.frame(
			Year = i, 
			X@output[X@output$Eventyear <= i , ]
		))
	}
	X@output <- out
	X@marginal <- c(TRUE, X@marginal) # Add Year to marginal
	X <- oapply(X, cols = "Eventyear", fun = "sum", na.rm = TRUE)
	X@output$Year <- as.numeric(levels(X@output$Year)[X@output$Year])
	X@output <- X@output[!is.na(result(X)) , ]
	return(X)
}

objects.store(buildings, heatingEnergy, timepoints)

cat("Saved ovariables buildings, heatingEnergy, timepoints\n")