Building model: Difference between revisions

Revision as of 20:37, 15 February 2015

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Question

How to estimate the size of the building stock of a city, including heating properties, renovations etc? The situation is followed over time, and different policies can be implemented.

Answer

For examples of model use, see Building stock in Kuopio and Climate change policies and health in Kuopio.

Rationale

Calculations

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library(OpasnetUtils)

#########3# 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
#		"eventyear", # A dummy variable to combine time periods to numerical time axis.
		"obsyear" # A data.frame of years for which observations are calculated. This requires timepoints function.
	)),
	formula = function(...) {
		# Current building stock
		stock <- stockBuildings * heatingShares * efficiencyShares

		if(!"Eventyear" %in% colnames(stock@output)) { # Make a duplicate of Time because Eventyear is needed later.
			stock <- stock * Ovariable(output = data.frame(Eventyear = NA, Result = 1), marginal = c(TRUE, FALSE))
			stock@output$Eventyear <- stock@output$Time
		}
		
#		# Annual changes of floor amount in time multiplied by time, fraction of energy classes and heating types.
#		timelength <- Ovariable(
#			output = data.frame(
#				obsyear,
#				Result = c(diff(obsyear[[1]]), 0)
#			),
#			marginal = c(TRUE, FALSE)
#		)

		change <- changeBuildings * timelength
		change <- timepoints(change, obsyear, sumtimecol = FALSE) # Cumulative changes at certain timepoints.

		stock@output <- orbind(stock, change)
		rate <- stock * rateBuildings * rateHeatingShares * rateEfficiencyShares
		
		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$Age <- as.numeric(as.character(reno@output$Renovationyear)) - 
			as.numeric(as.character(reno@output$Eventyear))

		# Floor area of renovations in ten years. Combine with continuous index Age.
		reno <- continuousOps(reno, rateBuildings, '*') * renovationShares * 10 
		reno@output$Eventyear <- reno@output$Renovationyear # Renovation is the new event.
		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", # 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")

Dependencies

Exposure to PM2.5 in Finland
OpasnetUtils/Drafts
Energy use of buildings
Ovariables used or produced and example pages where they are used:
- Building stock in Kuopio
  - buildingStock, # Current building stock
  - renovation, # Percentage of renovations per year
  - renovationShares, # Fraction of renovation type when renovation is done.
  - construction, # Construction rate in the future
  - constructionAreas,
  - buildingTypes, # A dummy variable to combine two different indices: Building and Building2
  - heatingShares, # Heating types of current buildings
  - heatingSharesNew, # Heating types of the buildings in the future
  - buildings,
- Energy use of buildings
  - energyUse,
  - efficienciesNew, # Energy efficiencies in the future
  - savingPotential,
- Climate change policies and health in Kuopio
  - eventyear, # A dummy variable to combine time periods to numerical time axis.
  - efficiencies, # Energy efficiencies of current buildings
  - obsyear # Years for which observations are calculated. This requires timepoints function.

References

Building model: Difference between revisions

Revision as of 20:37, 15 February 2015

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Question

Answer

Rationale

Calculations

Dependencies

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References

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