Difference between revisions of "Building model"

library(OpasnetUtils)

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

buildings <- Ovariable("buildings", 
	dependencies = data.frame(Name = c(
		"stockBuildings", # Building stock given as an amount at given timepoints
		"heatingShares", # Heating types of current buildings
		"efficiencyShares", # Energy efficiency types of current buildings
		"changeBuildings", # Construction or demolition rate as floor-m2. Indices Efficiency and 
			# Heating should be included here, if used.
#		"buildingTypes", # A dummy variable to combine two different indices: Building and Building2
		"renovationShares", # Fraction of renovation type when renovation is done.
		"rateBuildings", # Percentage of renovations and other relative changes per year
		"year" # A data.frame of years for which observations are calculated. This requires timepoints function.
	)),
	formula = function(...) {
#		######## Current building stock
#		stock <- stockBuildings * heatingShares * efficiencyShares


#		#### Changes in building stock due to construction etc.
		
#		change <- timepoints(changeBuildings, obsyear, sumtimecol = FALSE) # Cumulative changes at certain timepoints.

		#### Calculate cb ie. those buildings that were built between Time-1 and Time.
		#### This has two parts: the stock (stockBuildings) is treated as net construction rate
		#### by assuming that after construction Time nothing is demolished.
		#### Then this is added to the direct construction data (changeBuildings).

#		### This code can be used to change cumulative building stock data to construction rates of periods.
#		stockBuildings@output$Time <- as.numeric(as.character(stockBuildings@output$Time))
#		years <- sort(unique(stockBuildings@output$Time))
#		cb <- stockBuildings
#		cb@output$Time <- years[match(cb@output$Time, years) + 1] # Frame shift with time, years 2:n.
#		cb@output <- subset(cb@output, !is.na(Time))
#		cb <- stockBuildings - cb
#		cb@output <- orbind(cb, subset(stockBuildings@output, Time = years[1])) # First year

		cb <- stockBuildings * heatingShares * efficiencyShares
#		colnames(cb@output)[colnames(cb@output) == "Time"] <- "Startyear"
		
		# Add buildings from stock and buildings from construction data

		cb <- orbind2(cb, changeBuildings)

		
		########### Renovations of existing buildings. # Combine with (continuous) index Age.

		reno <- cb * renovationShares
		marginals <- colnames(reno@output)[reno@marginal]
		reno@output$Age <- as.numeric(as.character(reno@output$Startyear)) - # Startyear is the time of renovation
			as.numeric(as.character(reno@output$Time)) # Time is the time of construction
		reno@marginal <- colnames(reno@output) %in% marginals

		reno@output <- reno@output[result(reno) != 0 , ] # Zeros are meaningless
		reno <- reno * renovationRate # continuousOps(reno, renovationRate, '*') FIX continuousOps before using
#		reno@output$Startyear <- reno@output$Time # Renovation is the new event.
		reno <- oapply(reno, cols = c("Age", "Time"), FUN = sum)
#		reno <- timepoints(reno, obstime) # Accumulate over observation time.

		out <- reno * -1 # Equal amount stops being non-renovated.
		out@output$Renovation <- "None"
#		out@output$Renovation <- as.factor(out@output$Renovation)
		out <- orbind2(out, reno) # Temp2: Construction + renovation in data.frame

		for(i in colnames(reno@output)[reno@marginal]) {
			out[[i]][is.na(out[[i]])] <- "Not known"
		}

		return(out)
	}
)

### HeatingEnergy

heatingEnergy <- Ovariable("heatingEnergy", 
	dependencies = data.frame(Name = c(
		"energyUse", # Energy consumption per floor area
		"efficiencyRatio", # Relative energy consumption compared with the efficiency group Old.
		"renovationRatio", # Relative energy consumption compared with the Renovation
		"buildings",
		"buildingTypes"
	)),
	formula = function(...) {
		
		out <- buildings * buildingTypes * energyUse * efficiencyRatio * renovationRatio
		
		return(out)
	}
)

####### Calculate emissions

emissions <- Ovariable("emissions", 
	dependencies = data.frame(
		Name = c(
			"heatingEnergy",
			"fuelTypes", 
			"emissionFactors"
		)
	),
	formula = function(...) {

		out <- oapply(heatingEnergy, cols = c("Building", "Efficiency"), FUN = sum)
		out <- out * fuelTypes * emissionFactors * 3.6  * 1E-9 # convert from kWh /a to MJ /a and mg to ton

		out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE)

		out@output$Emission_site <- as.factor(ifelse(
			out@output$Emission_site == "At site of consumption",
			as.character(out@output$City_area),
			as.character(out@output$Emission_site)
		))

		out <- oapply(
			out,
			cols = c("Heating", "Burner", "Fuel", "City_area"),
			FUN = sum
		)
	}
)
#"Emission_site", "Emission_height", 

exposure <- Ovariable("exposure", 
	# emis is in ton /a
	# iF = conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration
	dependencies = data.frame(Name = c("emis2", "iF", "pop")),
	formula = function(...) {
		BR <- 20 # Nominal breathing rate (m^3 /d)
		BR <- BR / 24 / 3600 # m^3 /s
		out <- 1E+12 / 365 / 24 / 3600 # Emission scaling from ton /a to ug /s.
		out <- (emis2 * out) * iF / BR / pop # the actual equation
		out <- unkeep(out, prevresults = TRUE, sources = TRUE)
		return(out)
	}
)

objects.store(buildings, heatingEnergy, emissions, exposure)

cat("Saved ovariables buildings, heatingEnergy, emissions, exposure\n")