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 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
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)	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

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Causal diagram of the building model.

Calculations

+ Show code- Hide code 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$Emission_site) )) out <- oapply( out, cols = c("Burner", "City_area"), FUN = sum ) return(out) } ) exposure <- Ovariable("exposure", dependencies = data.frame( Name = c( "emissions", # is in ton /a "iF", # conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration "population" ), Ident = c(NA, "Op_en3435/initiate", NA) ), formula = function(...) { BR <- 20 # Nominal breathing rate (m^3 /d) BR <- BR / 24 / 3600 # m^3 /s out <- 1E+12 / 365 / 24 / 3600 # Emission scaling from ton /a to ug /s. out <- (emissions * out) * iF / BR / population # the actual equation out <- unkeep(out, prevresults = TRUE, sources = TRUE) out@output <- out@output[!out@output$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

• Exposure to PM2.5 in Finland
• OpasnetUtils/Drafts
• Energy use of buildings
• Building stock in Kuopio

See also

• Building stock in Kuopio
• Building stock in Europe
• Building data availability in Kuopio
• Energy balance
• Greenhouse gas emissions in Kuopio
• heande:File:2013 10 29 - Exposure-Response Functions.xlsx
• heande:Health impact assessment framework
• heande:File:2013 10 29 WP6.2 Agreed ER.doc
• heande:Urgenche: Workpackage Exposure, Health, and Well-being
• heande:File:WP6 Deliverable 6 1.doc
• Land use in Kuopio
• City and energy solutions from Siemens:
  • Mobile web app
  • Smart grid
  • Smart data in Aspern
  • Environmental awareness in Vienna
  • Transforming cities through sustainable technology

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