Building model

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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.

The model uses the following ovariables and their inputs:

**Variables in the assessment model**
Ovariable	Dependencies	Measure	Indices	Missing data
buildings (from the model)	stockBuildings (case-specific data from the user) e.g. Building stock in Kuopio	Amount of building stock (typically in floor-m2) at given timepoints.	Required indices: Time (time the building was built. If not known, present year can be used for all buildings.) Typical indices: City_area, Building (building type)	You must give either stockBuildings, heatingShares, and efficiencyShares or changeBuildings or both. For missing data, use 0.
	heatingShares (case-specific data from the user)	Fractions of heating types. Should sum up to 1 within each group defined by optional indices.	Required indices: Heating. Typical indices: Time, Building	If no data, use 1 as a placeholder.
	efficiencyShares (case-specific data from the user)	Fraction of energy efficiency types. Should sum up to 1 for each group defined by other indices.	Required indices: Efficiency. Typical indices: Time, Building.	If no data, use 1 as default.
	changeBuildings (case-specific data from the user)	Construction or demolition rate as floor-m2 at given timepoints.	Required indices: Time, Efficiency, Heating. If both stockBuildings and changeBuildings are used, changeBuildings should have all indices in stockBuildings, heatingShares, and efficiencyShares. Typical indices: Building, City_area.	If the data is only in stockBuildings, use 0 here.
	renovationShares (case-specific data from the user)	Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices.	Required indices: Renovation, Startyear. Startyear is the time when the renovation is done, and it must be different than the Time index. Typical indices:	If no data, use 1 as default.
	renovationRate (case-specific data from user. You can also use fairly generic data from Buildging stock in Kuopio.)	Rate of renovation (fraction per time unit).	Required indices: Age (the time difference between construction and renovation, i.e. Startyear - Time for each building).
	obstime (assessment-specific years of interest)	one-column data.frame about the years to be used in output. The column name must be the same as the index name in RenovationShares (typically Startyear). E.g. Climate change policies and health in Kuopio
heatingEnergy (from the model):	buildings (from the model; see above).
	energyUse (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)	Energy consumption per floor area (kWh / m2 /a)	Required indices: - . Typical indices: Building, Heating.	if this data is missing, you can only calculate building stock but nothing further.
	efficiencyRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)	Relative energy consumption compared with the efficiency group Old.	Required indices: Efficiency. Typical indices: Time, Building.	If no data, use 1 as default.
	renovationRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings)	Relative energy consumption compared with the Renovation location None.	Required indices: Renovation. Typical indices: Building.	If no data, use 1 as default.
emissions (from the model) (emissions in mass per time):	heatingEnergy (from the model; see above)
	fuelShares (fairly generic knowledge from e.g. Energy balance in Kuopio ----#: . Currently this data is on page Emission factors for burning processes Table Fuel use in different heating types. However, this is clearly case-specific data and should be on a case-specific page. This should be done retrospectively to Kuopio and Basel as well. --Jouni (talk) 09:22, 24 May 2015 (UTC) (type: truth; paradigms: science: comment)	Tells how much of fuel is used for a certain neating energy need.	Required indices: Fuel_type. Typical indices:
	emissionFactors (generic information, but may be cultural differences. E.g. Emission factors for burning processes ##	emissions per unit of energy produced (g / J or similar unit)	Required indices: Exposure_agent. Typical indices: Emission_height.
exposure (from the model) is in ug/m3 in ambient air average concentration:	emissions (from the model; see above) is in ton /a		Required indices: - . Typical indices: Time, City_area, Exposure_agent, Emission_height.
	iF (generic data but depends on population density, emission height etc)	conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure	Required indices: Typical indices:
	population	Amount of population exposed.	Required indices: - . Typical indices: Time, Area

Rationale

Calculations

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library(OpasnetUtils)

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

buildings <- Ovariable("buildings", 
	dependencies = data.frame(Name = c(
		"stockBuildings",
		"heatingShares",
		"efficiencyShares",
		"changeBuildings",
		"renovationShares", 
		"renovationRate", 
		"obstime",
		"heating_before", # Should heatingShares be calculated before renovate and timepoints (or after)? 
		"efficiency_before" # Should efficiencyShares be calculated before renovate and timepoints (or after)?
	)),
	formula = function(...) {

		#### 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
		
		######## This function applies renovations to the building stock and then follows it in time.

		renovate <- function(
			renoStock, # building stock to be renovated
			renovationShares, # shares of different renovations (see Answer)
			renovationRate, # renovation rate (see Answer)
			obstime # observation times (see Answer)
		) {
			reno <- renoStock * 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 <- reno * renovationRate # continuousOps(reno, renovationRate, '*') FIX continuousOps before using
			reno <- oapply(reno, cols = c("Age", "Time"), FUN = sum)

			out <- reno * -1 # Equal amount stops being non-renovated.
			out@output$Renovation <- "None"
			out <- orbind2(out, reno, use_fillna = TRUE) # Renovated buildings
			colnames(renoStock@output)[colnames(renoStock@output) == "Time"] <- "Startyear"
			out <- orbind2(
				out, 
				renoStock * Ovariable( # Add non-renovated buildings
					output = data.frame(Renovation = "None", Result = 1), 
					marginal = c(TRUE, FALSE),
				use_fillna = TRUE
				)
			)
	
			######## NA in indices will ruin timepoints. Therefore must do fillna but with warning.

			b <- character()
			for(i in colnames(out@output)[out@marginal]) {if(any(is.na(out@output[[i]]))) b <- c(b, i)}
			if(length(b) > 0) {
				out@output <- fillna(out@output, b)
				warning("Missing values had to be filled for timepoints by function fillna in indices: ", b, "\n")
			}

			out <- timepoints(out, obstime) # Accumulate over observation time.

			return(out)
		}

		########### Renovations of existing buildings. Heating and efficiency are applied afterwards,
		# so that they may change in the existing stock.

		stock <- stockBuildings

		if(heating_before) stock <- stock * heatingShares
		if(efficiency_before) stock <- stock * efficiencyShares

		stock <- renovate(stock, renovationShares, renovationRate, obstime)

		if(!heating_before) stock <- stock * heatingShares
		if(!efficiency_before) stock <- stock * efficiencyShares

		########### Renovations of constructed buildings. These must contain the heating and efficiency
		# data, and those cannot be changed afterwards.

		change <- renovate(changeBuildings, renovationShares, renovationRate, obstime)
		
		# Add buildings from stock and buildings from construction data

		out <- orbind2(
			stock, 
			change, 
			use_fillna = TRUE, 
			warn = "This should NOT happen: stock and change don't match when evaluating buildings"
		)
		out@output$Time <- as.numeric(as.character(out@output$Time))

		return(out)
	}
)

### HeatingEnergy

heatingEnergy <- Ovariable("heatingEnergy", 
	dependencies = data.frame(Name = c(
		"energyUse",
		"efficiencyRatio",
		"renovationRatio",
		"buildings"
	)),
	formula = function(...) {
		
		out <- buildings * energyUse * efficiencyRatio * renovationRatio
		out <- unkeep(out, prevresults = TRUE, sources = TRUE)
		out <- oapply(out, cols = c("Building"), FUN = sum)
		
		return(out)
	}
)

####### Calculate emissions

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

		out <- oapply(heatingEnergy, cols = c("Building", "Efficiency"), FUN = sum)
		out <- out * fuelShares * 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("Burner", "City_area"),
			FUN = sum
		)
		return(out)
	}
)

exposure <- Ovariable("exposure", 
	dependencies = data.frame(Name = c(
		"emissions", #  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
		"population"
	)),
	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 <- (emissions * out) * iF / BR / population # the actual equation
		out <- unkeep(out, prevresults = TRUE, sources = TRUE)
		out@output <- out@output[!out@output$Pollutant %in% c("CO2", "CO2official") , ]
		colnames(out@output)[colnames(out@output) == "Pollutant"] <- "Exposure_agent"
		out <- oapply(out, cols = c("Renovation"), FUN = sum)

		return(out)
	}
)

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

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

Dependencies

References

Sundell, J., Levin, H., Nazaroff, W. W., Cain, W. S., Fisk, W. J., Grimsrud, D. T., Gyntelberg, F., Li, Y., Persily, A. K., Pickering, A. C., Samet, J. M., Spengler, J. D., Taylor, S. T., Weschler, C. J., Ventilation rates and health: multidisciplinary review of the scientific literature. INDOOR AIR 21 (2011) 3: 191 - 204. doi:10.1111/j.1600-0668.2010.00703.x ISSN 0905-6947
Brightman, H. S., Milton, D. K., Wypij, D., Burge, H. A., Spengler, J. D. Evaluating building-related symptoms using the US EPA BASE study results. INDOOR AIR 18 (2008) 4: 335-345. doi:10.1111/j.1600-0668.2008.00557.x
Nishioka, Y, Levy, JI, Norris, GA, Bennett, DH, Spengler, JD. A risk-based approach to health impact assessment for input-output analysis - Part 2: Case study of insulation. INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT 10 (2005) 4: 255-262. doi:10.1065/lca2004.10.186.2 ISSN 0948-3349
Samet, JM, Spengler, JD. Indoor environments and health: Moving into the 21st century. AMERICAN JOURNAL OF PUBLIC HEALTH 93 (2003) 9: 1489-1493. 10.2105/AJPH.93.9.1489 doi:DI 10.2105/AJPH.93.9.1489 ISSN 0090-0036
Nishioka, Y, Levy, JI, Norris, GA, Wilson, A, Hofstetter, P, Spengler, JD. Integrating risk assessment and life cycle assessment: A case study of insulation RISK ANALYSIS 22 (2002) 5: 1003-1017. doi:10.1111/1539-6924.00266 ISSN 0272-4332