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:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle buildings = (stockBuildings + changeBuildings) * heatingShares * efficiencyShares * renovationShares * renovationRate}

**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 Building 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

Rationale

Calculations

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### This code is Op_en6289/buildingstest on page [[Building model]].

library(OpasnetUtils)

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

buildings <- Ovariable("buildings", 
	dependencies = data.frame(Name = c(
		"stockBuildings",
		"changeBuildings",
		"heatingShares", # This can be indexed by building year (Time) or Observation year (Obsyear)
		"efficiencyShares", # Same here
		"renovationShares", 
		"renovationRate", # Fraction of buildings renovated between timepoints
		"obstime" # In the new version obstime is ovariable with column Obsyear.
	)),
	formula = function(...) {

		stock <- stockBuildings * obstime * heatingShares * efficiencyShares

		stock2 <- changeBuildings * obstime # Must contain data about heatings and efficiencies
		stock2@output$Time <- as.factor(stock2@output$Time) # Also stock@output$Time is factor
		buil <- combine(stock, stock2) # Buildings in the whole timeline
		buil <- unkeep(buil, sources = TRUE)
		buil@output$Age <- as.numeric(as.character(buil@output$Obsyear)) - as.numeric(as.character(buil@output$Time))
		buil@marginal <- c(buil@marginal, TRUE)
		buil@output <- buil@output[buil@output$Age >= 0 , ]

		# Note that if stockBuildings and changeBuildings have different marginals (typically policies),
		# there will be NA in these indices. This will be corrected below.
		for(i in colnames(buil@output)[buil@marginal]) {
			if(any(is.na(buil@output[[i]]))) {
				buil@output <- fillna(buil@output, i)
				print(paste("Column", i, "treated with fillna."))
			}
		}
		renovate <- (1 - renovationRate) * (1 - renovationShares) # Assumes that data has row for Renovation: None = 0
		renovate@output <- renovate@output[renovate@output$Renovation == "None" , ]
		renovate <- combine(renovate, renovationRate * renovationShares)
		
		out <- data.frame()
		prevreno <- unkeep(buil * renovate * 0, sources = TRUE) # Previously renovated buildings for the first time point
		prevreno@output <- prevreno@output[prevreno@output$Renovation != "None" , ][1,] # Just take one renovated example row
		marginals <- colnames(prevreno@output)[prevreno@marginal]
		
		for(i in obstime@output$Obsyear) { # Accumulate the building stock
			# Take the building stock
			temp <- buil
			temp@output <- temp@output[temp@output$Obsyear == i , ] # Take buildings of year i
			prevreno@output$Obsyear <- i # update the observation year of the previous renovation
			# Subtrack from the building stock buildings that are already renovated.
			temp <- combine(temp, -1 * oapply(prevreno, cols = "Renovation", FUN = sum)) # Subtract previously renovated
			reno <- unkeep(temp * renovate, sources = TRUE) # Renovate the current non-renovated stock
			# If renovate does not match with this timepoint, mark all buildings here as unrenovated.
			# Is there new renovation in this time point?
			if(sum(result(reno)) == 0) {
				reno <- temp * Ovariable(
					output = data.frame(Renovation = "None", Result = 1), 
					marginal = c(TRUE, FALSE)
				)
			}
			newreno <- combine(prevreno, reno) # Renovated buildings in this time point.
			# Take the previous timepoints, and previously renovated and now possibly renovated together.
			out <- rbind(newreno@output, out) # Add previously and now renovated together
			prevreno <- unkeep(newreno, sources = TRUE)
			prevreno@output <- prevreno@output[prevreno@output$Renovation != "None" , ]
		}
		out <- Ovariable(output = out, marginal = colnames(out) %in% marginals)
		out <- oapply(unkeep(out, sources = TRUE), cols = c("Age", "Time"), FUN = sum)
		colnames(out@output)[colnames(out@output) == "Obsyear"] <- "Time" # From now on, Time means the time of observation
		out@output$Time <- as.numeric(as.character(out@output$Time))
		
		return(out)
	}
)

objects.store(buildings)

cat("Saved ovariable buildings\n")

Dependencies

**Helsinki energy decision 2015**
In English
Assessment	Main page \| Helsinki energy decision options 2015
Helsinki data	Building stock in Helsinki \| Helsinki energy production \| Helsinki energy consumption \| Energy use of buildings \| Emission factors for burning processes \| Prices of fuels in heat production \| External cost
Models	Building model \| Energy balance \| Health impact assessment \| Economic impacts
Related assessments	Climate change policies in Helsinki \| Climate change policies and health in Kuopio \| Climate change policies in Basel
In Finnish
Yhteenveto	Helsingin energiapäätös 2015 \| Helsingin energiapäätöksen vaihtoehdot 2015 \| Helsingin energiapäätökseen liittyviä arvoja \| Helsingin energiapäätös 2015.pptx

**Urgenche research project 2011 - 2014: city-level climate change mitigation**
Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) Relevant data Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings Relevant methods Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎ City Kuopio Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing City Basel Buildings in Basel (password-protected) Energy balances Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou

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